Merge branch 'kvcache-ai:main' into main

csrc/balance_serve/sched/utils/statistics.hpp

+#ifndef STATISTICS_HPP
+#define STATISTICS_HPP
+
+#include <chrono>
+#include <iostream>
+#include <string>
+#include <unordered_map>
+
+class Statistics {
+public:
+  // Increment the counter for a given key by a specified value (default is 1)
+  void increment_counter(const std::string &key, int64_t value = 1) {
+    counters_[key] += value;
+  }
+
+  int64_t &get_counter(const std::string &key) { return counters_[key]; }
+
+  // Start the timer for a given key
+  void start_timer(const std::string &key) {
+    active_timers_[key] = std::chrono::high_resolution_clock::now();
+  }
+
+  // Stop the timer for a given key and update the total time and count
+  void stop_timer(const std::string &key) {
+    auto start_it = active_timers_.find(key);
+    if (start_it != active_timers_.end()) {
+      auto duration =
+          std::chrono::high_resolution_clock::now() - start_it->second;
+      timings_[key].total_time += duration;
+      timings_[key].count += 1;
+      active_timers_.erase(start_it);
+    } else {
+      // Handle error: stop_timer called without a matching start_timer
+      std::cerr << "Warning: stop_timer called for key '" << key
+                << "' without a matching start_timer.\n";
+    }
+  }
+
+  // Print out the collected statistical information
+  void report() const {
+    std::cout << "Counters:\n";
+    for (const auto &kv : counters_) {
+      std::cout << "  " << kv.first << ": " << kv.second << "\n";
+    }
+    std::cout << "\nTimers:\n";
+    for (const auto &kv : timings_) {
+      std::cout << "  " << kv.first << ": count = " << kv.second.count
+                << ", total_time = " << kv.second.total_time.count() << "s"
+                << ", average_time = "
+                << (kv.second.count > 0
+                        ? kv.second.total_time.count() / kv.second.count
+                        : 0)
+                << "s\n";
+    }
+  }
+
+private:
+  // Mapping from key to counter
+  std::unordered_map<std::string, int64_t> counters_;
+
+  // Struct to hold timing information for a key
+  struct TimingInfo {
+    int64_t count = 0;
+    std::chrono::duration<double> total_time =
+        std::chrono::duration<double>::zero();
+  };
+
+  // Mapping from key to timing information
+  std::unordered_map<std::string, TimingInfo> timings_;
+
+  // Mapping from key to the start time of active timers
+  std::unordered_map<std::string,
+                     std::chrono::high_resolution_clock::time_point>
+      active_timers_;
+};
+
+#endif // STATISTICS_HPP

csrc/balance_serve/sched/utils/timer.hpp

+#pragma once
+#include "readable_number.hpp"
+#include <cassert>
+#include <chrono>
+#include <iomanip>
+#include <iostream>
+#include <map>
+#include <sstream>
+#include <string>
+
+inline std::string doubleToStringR2(double value) {
+  std::stringstream stream;
+  stream << std::fixed << std::setprecision(2) << value;
+  return stream.str();
+}
+
+class Timer {
+public:
+  std::string name;
+  bool tmp_timer = false;
+
+  Timer() {}
+  Timer(std::string name) : name(name), tmp_timer(true) { start(); }
+  ~Timer() {
+    if (tmp_timer) {
+      std::cout << name << " " << elapsedMs() << " ms" << std::endl;
+    }
+  }
+
+  void start() {
+    m_startTime = std::chrono::high_resolution_clock::now();
+    assert(m_isRunning == false);
+    m_isRunning = true;
+  }
+
+  void stop() {
+    m_endTime = std::chrono::high_resolution_clock::now();
+    assert(m_isRunning == true);
+    m_isRunning = false;
+    m_runningNs += elapsedNs();
+  }
+
+  double elapsedNs() {
+    std::chrono::time_point<std::chrono::high_resolution_clock> endTime;
+
+    if (m_isRunning) {
+      endTime = std::chrono::high_resolution_clock::now();
+    } else {
+      endTime = m_endTime;
+    }
+
+    return std::chrono::duration_cast<std::chrono::nanoseconds>(endTime -
+                                                                m_startTime)
+        .count();
+  }
+
+  void printElapsedMilliseconds() {
+    std::cout << elapsedNs() / 1e6 << " ms" << std::endl;
+  }
+
+  static std::string ns_to_string(double duration) {
+    auto nano_sec = duration;
+    if (nano_sec >= 1000) {
+      auto mirco_sec = nano_sec / 1000.0;
+      if (mirco_sec >= 1000) {
+        auto milli_sec = mirco_sec / 1000.0;
+        if (milli_sec >= 1000) {
+          auto seconds = milli_sec / 1000.0;
+
+          if (seconds >= 60.0) {
+            auto minutes = seconds / 60.0;
+
+            if (minutes >= 60.0) {
+              auto hours = minutes / 60.0;
+              return doubleToStringR2(hours) + " h";
+            } else {
+              return doubleToStringR2(minutes) + " min";
+            }
+          } else {
+            return doubleToStringR2(seconds) + " sec";
+          }
+        } else {
+          return doubleToStringR2(milli_sec) + " ms";
+        }
+      } else {
+        return doubleToStringR2(mirco_sec) + " us";
+      }
+    } else {
+      return doubleToStringR2(nano_sec) + " ns";
+    }
+  }
+
+  double runningTimeNs() { return m_runningNs; }
+
+  std::string runningTime() {
+    auto duration = m_runningNs;
+    return ns_to_string(duration);
+  }
+
+  std::string elapsedTime() { return ns_to_string(elapsedNs()); }
+  double elapsedMs() { return elapsedNs() / 1e6; }
+  std::string report_throughput(size_t op_cnt) {
+    double ops = op_cnt / elapsedMs() * 1000;
+    return readable_number(ops) + "op/s";
+  }
+
+  void merge(Timer &other) {
+    assert(m_isRunning == false);
+    assert(other.m_isRunning == false);
+    m_runningNs += other.runningTimeNs();
+  }
+
+private:
+  std::chrono::time_point<std::chrono::high_resolution_clock> m_startTime;
+  std::chrono::time_point<std::chrono::high_resolution_clock> m_endTime;
+  bool m_isRunning = false;
+  double m_runningNs = 0.0;
+};
+
+class Counter {
+public:
+  Counter() {}
+
+  std::map<std::string, size_t> counters;
+
+  void inc(const char *name, size_t num) { counters[name] += num; };
+  void print() {
+    for (auto &p : counters) {
+      std::cout << p.first << " : " << p.second << std::endl;
+    }
+  };
+};

csrc/custom_marlin/binding.cpp

+/**
+ * @Description  :
+ * @Author       : Azure-Tang
+ * @Date         : 2024-07-25 13:38:30
+ * @Version      : 1.0.0
+ * @LastEditors  : kkk1nak0
+ * @LastEditTime : 2024-08-12 03:05:04
+ * @Copyright (c) 2024 by KVCache.AI, All Rights Reserved.
+ **/
+
+#include "gptq_marlin/ops.h"
+// Python bindings
+#include <pybind11/pybind11.h>
+#include <pybind11/stl.h>
+#include <torch/extension.h>
+#include <torch/library.h>
+#include <torch/torch.h>
+// namespace py = pybind11;
+
+PYBIND11_MODULE(vLLMMarlin, m) {
+
+    /*m.def("dequantize_q8_0", &dequantize_q8_0, "Function to dequantize q8_0
+    data.", py::arg("data"), py::arg("blk_size"), py::arg("device"));
+    m.def("dequantize_q6_k", &dequantize_q6_k, "Function to dequantize q6_k
+    data.", py::arg("data"), py::arg("blk_size"), py::arg("device"));
+    m.def("dequantize_q5_k", &dequantize_q5_k, "Function to dequantize q5_k
+    data.", py::arg("data"), py::arg("blk_size"), py::arg("device"));
+    m.def("dequantize_q4_k",  &dequantize_q4_k, "Function to dequantize q4_k
+    data.", py::arg("data"), py::arg("blk_size"), py::arg("device"));
+    m.def("dequantize_q3_k",  &dequantize_q3_k, "Function to dequantize q3_k
+    data.", py::arg("data"), py::arg("blk_size"), py::arg("device"));
+    m.def("dequantize_q2_k",  &dequantize_q2_k, "Function to dequantize q2_k
+    data.", py::arg("data"), py::arg("blk_size"), py::arg("device"));
+    m.def("dequantize_iq4_xs",  &dequantize_iq4_xs, "Function to dequantize
+    iq4_xs data.", py::arg("data"), py::arg("blk_size"), py::arg("device"));*/
+    m.def("gptq_marlin_gemm", &gptq_marlin_gemm,
+          "Function to perform GEMM using Marlin quantization.", py::arg("a"),
+          py::arg("b_q_weight"), py::arg("b_scales"), py::arg("g_idx"),
+          py::arg("perm"), py::arg("workspace"), py::arg("num_bits"), py::arg("size_m_tensor"),
+          py::arg("size_m"), py::arg("size_n"), py::arg("size_k"),
+          py::arg("sms"), py::arg("is_k_full"));
+    m.def("gptq_marlin_repack", &gptq_marlin_repack,
+            "gptq_marlin repack from GPTQ");
+}
\ No newline at end of file

csrc/custom_marlin/gptq_marlin/gptq_marlin.cu

+/*
+ * Modified by Neural Magic
+ * Copyright (C) Marlin.2024 Elias Frantar
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *         http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+ /*
+  * Adapted from https://github.com/IST-DASLab/marlin
+  */
+  /*
+   * Adapted from
+   * https://github.com/vllm-project/vllm/tree/main/csrc/quantization/gptq_marlin
+   */
+#include "gptq_marlin.cuh"
+#include "gptq_marlin_dtypes.cuh"
+#include <c10/cuda/CUDAGuard.h>
+#define STATIC_ASSERT_SCALAR_TYPE_VALID(scalar_t)                              \
+    static_assert(std::is_same<scalar_t, half>::value ||                       \
+                      std::is_same<scalar_t, nv_bfloat16>::value,              \
+                  "only float16 and bfloat16 is supported");
+
+template <typename T> inline std::string str(T x) { return std::to_string(x); }
+
+namespace gptq_marlin {
+
+#if defined(__CUDA_ARCH__) && __CUDA_ARCH__ < 800
+
+    __global__ void permute_cols_kernel(int4 const* __restrict__ a_int4_ptr,
+        int const* __restrict__ perm_int_ptr,
+        int4* __restrict__ out_int4_ptr, int size_m,
+        int size_k, int block_rows) {}
+
+    template <typename scalar_t,         // compute dtype, half or nv_float16
+        const int num_bits,        // number of bits used for weights
+        const int threads,         // number of threads in a threadblock
+        const int thread_m_blocks, // number of 16x16 blocks in the m
+        // dimension (batchsize) of the
+        // threadblock
+        const int thread_n_blocks, // same for n dimension (output)
+        const int thread_k_blocks, // same for k dimension (reduction)
+        const int stages, // number of stages for the async global->shared
+        // fetch pipeline
+        const bool has_act_order,   // whether act_order is enabled
+        const int group_blocks = -1 // number of consecutive 16x16 blocks
+        // with a separate quantization scale
+    >
+    __global__ void
+        Marlin(const int4* __restrict__ A, // fp16 input matrix of shape mxk
+            const int4* __restrict__ B, // 4bit quantized weight matrix of shape kxn
+            int4* __restrict__ C,       // fp16 output buffer of shape mxn
+            const int4* __restrict__ scales_ptr, // fp16 quantization scales of shape
+            // (k/groupsize)xn
+            const int* __restrict__ g_idx, // int32 group indices of shape k
+            int num_groups, // number of scale groups per output channel
+            int prob_m,     // batch dimension m
+            int prob_n,     // output dimension n
+            int prob_k,     // reduction dimension k
+            int* locks      // extra global storage for barrier synchronization
+        ) {}
+
+} // namespace gptq_marlin
+
+torch::Tensor gptq_marlin_gemm(torch::Tensor& a, torch::Tensor& b_q_weight,
+    torch::Tensor& b_scales, torch::Tensor& g_idx,
+    torch::Tensor& perm, torch::Tensor& workspace,
+    int64_t num_bits, int64_t size_m, int64_t size_n,
+    int64_t size_k, bool is_k_full) {
+    TORCH_CHECK_NOT_IMPLEMENTED(false,
+        "marlin_gemm(..) requires CUDA_ARCH >= 8.0");
+    return torch::empty({ 1, 1 });
+}
+
+#else
+
+    // m16n8k16 tensor core mma instruction with fp16 inputs and fp32
+    // output/accumulation.
+    template <typename scalar_t>
+    __device__ inline void mma(const typename ScalarType<scalar_t>::FragA& a_frag,
+        const typename ScalarType<scalar_t>::FragB& frag_b,
+        typename ScalarType<scalar_t>::FragC& frag_c) {
+        const uint32_t* a = reinterpret_cast<const uint32_t*>(&a_frag);
+        const uint32_t* b = reinterpret_cast<const uint32_t*>(&frag_b);
+        float* c = reinterpret_cast<float*>(&frag_c);
+        if constexpr (std::is_same<scalar_t, half>::value) {
+            asm volatile(
+                "mma.sync.aligned.m16n8k16.row.col.f32.f16.f16.f32 "
+                "{%0,%1,%2,%3}, {%4,%5,%6,%7}, {%8,%9}, {%10,%11,%12,%13};\n"
+                : "=f"(c[0]), "=f"(c[1]), "=f"(c[2]), "=f"(c[3])
+                : "r"(a[0]), "r"(a[1]), "r"(a[2]), "r"(a[3]), "r"(b[0]), "r"(b[1]),
+                "f"(c[0]), "f"(c[1]), "f"(c[2]), "f"(c[3]));
+        }
+        else if constexpr (std::is_same<scalar_t, nv_bfloat16>::value) {
+            asm volatile(
+                "mma.sync.aligned.m16n8k16.row.col.f32.bf16.bf16.f32 "
+                "{%0,%1,%2,%3}, {%4,%5,%6,%7}, {%8,%9}, {%10,%11,%12,%13};\n"
+                : "=f"(c[0]), "=f"(c[1]), "=f"(c[2]), "=f"(c[3])
+                : "r"(a[0]), "r"(a[1]), "r"(a[2]), "r"(a[3]), "r"(b[0]), "r"(b[1]),
+                "f"(c[0]), "f"(c[1]), "f"(c[2]), "f"(c[3]));
+        }
+        else {
+            STATIC_ASSERT_SCALAR_TYPE_VALID(scalar_t);
+        }
+    }
+
+    // Instruction for loading a full 16x16 matrix fragment of operand A from shared
+    // memory, directly in tensor core layout.
+    template <typename scalar_t>
+    __device__ inline void ldsm4(typename ScalarType<scalar_t>::FragA& frag_a,
+        const void* smem_ptr) {
+        uint32_t* a = reinterpret_cast<uint32_t*>(&frag_a);
+        uint32_t smem = static_cast<uint32_t>(__cvta_generic_to_shared(smem_ptr));
+        asm volatile(
+            "ldmatrix.sync.aligned.m8n8.x4.shared.b16 {%0,%1,%2,%3}, [%4];\n"
+            : "=r"(a[0]), "=r"(a[1]), "=r"(a[2]), "=r"(a[3])
+            : "r"(smem));
+    }
+
+    // Lookup-table based 3-input logical operation; explicitly used for
+    // dequantization as the compiler does not seem to automatically recognize it in
+    // all cases.
+    template <int lut> __device__ inline int lop3(int a, int b, int c) {
+        int res;
+        asm volatile("lop3.b32 %0, %1, %2, %3, %4;\n"
+            : "=r"(res)
+            : "r"(a), "r"(b), "r"(c), "n"(lut));
+        return res;
+    }
+
+    // Constructs destination register by taking bytes from 2 sources (based on
+    // mask)
+    template <int start_byte, int mask>
+    __device__ inline uint32_t prmt(uint32_t a) {
+        uint32_t res;
+        asm volatile("prmt.b32 %0, %1, %2, %3;\n"
+            : "=r"(res)
+            : "r"(a), "n"(start_byte), "n"(mask));
+        return res;
+    }
+
+    // Efficiently dequantize an int32 value into a full B-fragment of 4 fp16
+    // values. We mostly follow the strategy in the link below, with some small
+    // changes:
+    // - FP16:
+    // https://github.com/NVIDIA/FasterTransformer/blob/release/v5.3_tag/src/fastertransformer/cutlass_extensions/include/cutlass_extensions/interleaved_numeric_conversion.h#L215-L287
+    // - BF16:
+    // https://github.com/NVIDIA/FasterTransformer/blob/release/v5.3_tag/src/fastertransformer/cutlass_extensions/include/cutlass_extensions/interleaved_numeric_conversion.h#L327-L385
+    template <typename scalar_t>
+    __device__ inline typename ScalarType<scalar_t>::FragB dequant_4bit(int q) {
+        STATIC_ASSERT_SCALAR_TYPE_VALID(scalar_t);
+    }
+
+    template <>
+    __device__ inline typename ScalarType<half>::FragB dequant_4bit<half>(int q) {
+        const int LO = 0x000f000f;
+        const int HI = 0x00f000f0;
+        const int EX = 0x64006400;
+        // Guarantee that the `(a & b) | c` operations are LOP3s.
+        int lo = lop3<(0xf0 & 0xcc) | 0xaa>(q, LO, EX);
+        int hi = lop3<(0xf0 & 0xcc) | 0xaa>(q, HI, EX);
+        // We want signed int4 outputs, hence we fuse the `-8` symmetric zero point
+        // directly into `SUB` and `ADD`.
+        const int SUB = 0x64086408;
+        const int MUL = 0x2c002c00;
+        const int ADD = 0xd480d480;
+        typename ScalarType<half>::FragB frag_b;
+        frag_b[0] = __hsub2(*reinterpret_cast<half2*>(&lo),
+            *reinterpret_cast<const half2*>(&SUB));
+        frag_b[1] = __hfma2(*reinterpret_cast<half2*>(&hi),
+            *reinterpret_cast<const half2*>(&MUL),
+            *reinterpret_cast<const half2*>(&ADD));
+        return frag_b;
+    }
+
+    template <>
+    __device__ inline typename ScalarType<nv_bfloat16>::FragB
+        dequant_4bit<nv_bfloat16>(int q) {
+        static constexpr uint32_t MASK = 0x000f000f;
+        static constexpr uint32_t EX = 0x43004300;
+
+        // Guarantee that the `(a & b) | c` operations are LOP3s.
+
+        int lo = lop3<(0xf0 & 0xcc) | 0xaa>(q, MASK, EX);
+        q >>= 4;
+        int hi = lop3<(0xf0 & 0xcc) | 0xaa>(q, MASK, EX);
+
+        typename ScalarType<nv_bfloat16>::FragB frag_b;
+        static constexpr uint32_t MUL = 0x3F803F80;
+        static constexpr uint32_t ADD = 0xC308C308;
+
+        frag_b[0] = __hfma2(*reinterpret_cast<nv_bfloat162*>(&lo),
+            *reinterpret_cast<const nv_bfloat162*>(&MUL),
+            *reinterpret_cast<const nv_bfloat162*>(&ADD));
+        frag_b[1] = __hfma2(*reinterpret_cast<nv_bfloat162*>(&hi),
+            *reinterpret_cast<const nv_bfloat162*>(&MUL),
+            *reinterpret_cast<const nv_bfloat162*>(&ADD));
+        return frag_b;
+    }
+
+    // Fast Int8ToFp16/Int8ToBf16: Efficiently dequantize 8bit int values to fp16 or
+    // bf16 Reference:
+    // - FP16:
+    // https://github.com/NVIDIA/FasterTransformer/blob/release/v5.3_tag/src/fastertransformer/cutlass_extensions/include/cutlass_extensions/interleaved_numeric_conversion.h#L53-L85
+    // - BF16:
+    // https://github.com/NVIDIA/FasterTransformer/blob/release/v5.3_tag/src/fastertransformer/cutlass_extensions/include/cutlass_extensions/interleaved_numeric_conversion.h#L125-L175
+    template <typename scalar_t>
+    __device__ inline typename ScalarType<scalar_t>::FragB dequant_8bit(int q) {
+        STATIC_ASSERT_SCALAR_TYPE_VALID(scalar_t);
+    }
+
+    template <>
+    __device__ inline typename ScalarType<half>::FragB dequant_8bit<half>(int q) {
+        static constexpr uint32_t mask_for_elt_01 = 0x5250;
+        static constexpr uint32_t mask_for_elt_23 = 0x5351;
+        static constexpr uint32_t start_byte_for_fp16 = 0x64646464;
+
+        uint32_t lo = prmt<start_byte_for_fp16, mask_for_elt_01>(q);
+        uint32_t hi = prmt<start_byte_for_fp16, mask_for_elt_23>(q);
+
+        static constexpr uint32_t I8s_TO_F16s_MAGIC_NUM = 0x64806480;
+
+        typename ScalarType<half>::FragB frag_b;
+        frag_b[0] =
+            __hsub2(*reinterpret_cast<half2*>(&lo),
+                *reinterpret_cast<const half2*>(&I8s_TO_F16s_MAGIC_NUM));
+        frag_b[1] =
+            __hsub2(*reinterpret_cast<half2*>(&hi),
+                *reinterpret_cast<const half2*>(&I8s_TO_F16s_MAGIC_NUM));
+        return frag_b;
+    }
+
+    template <>
+    __device__ inline typename ScalarType<nv_bfloat16>::FragB
+        dequant_8bit<nv_bfloat16>(int q) {
+        typename ScalarType<nv_bfloat16>::FragB frag_b;
+
+        float fp32_intermediates[4];
+        uint32_t* fp32_intermediates_casted =
+            reinterpret_cast<uint32_t*>(fp32_intermediates);
+
+        static constexpr uint32_t fp32_base = 0x4B000000;
+        fp32_intermediates_casted[0] = __byte_perm(q, fp32_base, 0x7650);
+        fp32_intermediates_casted[1] = __byte_perm(q, fp32_base, 0x7652);
+        fp32_intermediates_casted[2] = __byte_perm(q, fp32_base, 0x7651);
+        fp32_intermediates_casted[3] = __byte_perm(q, fp32_base, 0x7653);
+
+        fp32_intermediates[0] -= 8388736.f;
+        fp32_intermediates[1] -= 8388736.f;
+        fp32_intermediates[2] -= 8388736.f;
+        fp32_intermediates[3] -= 8388736.f;
+
+        uint32_t* bf16_result_ptr = reinterpret_cast<uint32_t*>(&frag_b);
+        bf16_result_ptr[0] = __byte_perm(fp32_intermediates_casted[0],
+            fp32_intermediates_casted[1], 0x7632);
+        bf16_result_ptr[1] = __byte_perm(fp32_intermediates_casted[2],
+            fp32_intermediates_casted[3], 0x7632);
+
+        return frag_b;
+    }
+
+    // Multiply dequantized values by the corresponding quantization scale; used
+    // only for grouped quantization.
+    template <typename scalar_t>
+    __device__ inline void scale(typename ScalarType<scalar_t>::FragB& frag_b,
+        typename ScalarType<scalar_t>::FragS& frag_s,
+        int i) {
+        using scalar_t2 = typename ScalarType<scalar_t>::scalar_t2;
+        scalar_t2 s = ScalarType<scalar_t>::num2num2(
+            reinterpret_cast<scalar_t*>(&frag_s)[i]);
+        frag_b[0] = __hmul2(frag_b[0], s);
+        frag_b[1] = __hmul2(frag_b[1], s);
+    }
+
+    // Same as above, but for act_order (each K is multiplied individually)
+    template <typename scalar_t>
+    __device__ inline void scale4(typename ScalarType<scalar_t>::FragB& frag_b,
+        typename ScalarType<scalar_t>::FragS& frag_s_1,
+        typename ScalarType<scalar_t>::FragS& frag_s_2,
+        typename ScalarType<scalar_t>::FragS& frag_s_3,
+        typename ScalarType<scalar_t>::FragS& frag_s_4,
+        int i) {
+        using scalar_t2 = typename ScalarType<scalar_t>::scalar_t2;
+        scalar_t2 s_val_1_2;
+        s_val_1_2.x = reinterpret_cast<scalar_t*>(&frag_s_1)[i];
+        s_val_1_2.y = reinterpret_cast<scalar_t*>(&frag_s_2)[i];
+
+        scalar_t2 s_val_3_4;
+        s_val_3_4.x = reinterpret_cast<scalar_t*>(&frag_s_3)[i];
+        s_val_3_4.y = reinterpret_cast<scalar_t*>(&frag_s_4)[i];
+
+        frag_b[0] = __hmul2(frag_b[0], s_val_1_2);
+        frag_b[1] = __hmul2(frag_b[1], s_val_3_4);
+    }
+
+    // Given 2 floats multiply by 2 scales (halves)
+    template <typename scalar_t>
+    __device__ inline void scale_float(float* c,
+        typename ScalarType<scalar_t>::FragS& s) {
+        scalar_t* s_ptr = reinterpret_cast<scalar_t*>(&s);
+        c[0] = __fmul_rn(c[0], ScalarType<scalar_t>::num2float(s_ptr[0]));
+        c[1] = __fmul_rn(c[1], ScalarType<scalar_t>::num2float(s_ptr[1]));
+    }
+
+    // Wait until barrier reaches `count`, then lock for current threadblock.
+    __device__ inline void barrier_acquire(int* lock, int count) {
+        if (threadIdx.x == 0) {
+            int state = -1;
+            do
+                // Guarantee that subsequent writes by this threadblock will be
+                // visible globally.
+                asm volatile("ld.global.acquire.gpu.b32 %0, [%1];\n"
+                    : "=r"(state)
+                    : "l"(lock));
+            while (state != count);
+        }
+        __syncthreads();
+    }
+
+    // Release barrier and increment visitation count.
+    __device__ inline void barrier_release(int* lock, bool reset = false) {
+        __syncthreads();
+        if (threadIdx.x == 0) {
+            if (reset) {
+                lock[0] = 0;
+                return;
+            }
+            int val = 1;
+            // Make sure that all writes since acquiring this barrier are visible
+            // globally, while releasing the barrier.
+            asm volatile("fence.acq_rel.gpu;\n");
+            asm volatile("red.relaxed.gpu.global.add.s32 [%0], %1;\n"
+                :
+            : "l"(lock), "r"(val));
+        }
+    }
+
+    // For a given "a" of size [M,K] performs a permutation of the K columns based
+    // on the given "perm" indices.
+    __global__ void permute_cols_kernel(int4 const* __restrict__ a_int4_ptr,
+        int const* __restrict__ perm_int_ptr,
+        int4* __restrict__ out_int4_ptr, int size_m,
+        int size_k, int block_rows) {
+        int start_row = block_rows * blockIdx.x;
+        int finish_row = start_row + block_rows;
+        if (finish_row > size_m) {
+            finish_row = size_m;
+        }
+        int cur_block_rows = finish_row - start_row;
+
+        int row_stride = size_k * sizeof(half) / 16;
+
+        auto permute_row = [&](int row) {
+            int iters = size_k / default_threads;
+            int rest = size_k % default_threads;
+
+            int offset = row * row_stride;
+
+            half const* a_row_half =
+                reinterpret_cast<half const*>(a_int4_ptr + offset);
+            half* out_half = reinterpret_cast<half*>(out_int4_ptr + offset);
+
+            int base_k = 0;
+
+            for (int i = 0; i < iters; i++) {
+                int cur_k = base_k + threadIdx.x;
+                int src_pos = perm_int_ptr[cur_k];
+
+                out_half[cur_k] = a_row_half[src_pos];
+
+                base_k += default_threads;
+            }
+
+            if (rest) {
+                if (threadIdx.x < rest) {
+                    int cur_k = base_k + threadIdx.x;
+                    int src_pos = perm_int_ptr[cur_k];
+
+                    out_half[cur_k] = a_row_half[src_pos];
+                }
+            }
+            };
+
+        for (int i = 0; i < cur_block_rows; i++) {
+            int cur_row = start_row + i;
+            if (cur_row < size_m) {
+                permute_row(cur_row);
+            }
+        }
+    }
+
+    template <typename scalar_t,         // compute dtype, half or nv_float16
+        const int num_bits,        // number of bits used for weights
+        const int threads,         // number of threads in a threadblock
+        const int thread_m_blocks, // number of 16x16 blocks in the m
+        // dimension (batchsize) of the
+        // threadblock
+        const int thread_n_blocks, // same for n dimension (output)
+        const int thread_k_blocks, // same for k dimension (reduction)
+        const int stages, // number of stages for the async global->shared
+        // fetch pipeline
+        const bool has_act_order,   // whether act_order is enabled
+        const int group_blocks = -1 // number of consecutive 16x16 blocks
+        // with a separate quantization scale
+    >
+    __device__ void
+        Marlin(const int4* __restrict__ A, // fp16 input matrix of shape mxk
+            const int4* __restrict__ B, // 4bit quantized weight matrix of shape kxn
+            int4* __restrict__ C,       // fp16 output buffer of shape mxn
+            const int4* __restrict__ scales_ptr, // fp16 quantization scales of shape
+            // (k/groupsize)xn
+            const int* __restrict__ g_idx, // int32 group indices of shape k
+            int num_groups, // number of scale groups per output channel
+            int prob_m,     // batch dimension m, should be divisible by (16 * thread_m_blocks) if bigger than that
+            int prob_n,     // output dimension n
+            int prob_k,     // reduction dimension k
+            int* locks      // extra global storage for barrier synchronization
+        ) {
+        // Each threadblock processes one "stripe" of the B matrix with (roughly) the
+        // same size, which might involve multiple column "slices" (of width 16 *
+        // `thread_n_blocks`). Stripes are defined as shown in the 3x3 matrix 5 SM
+        // example:
+        //   0 1 3
+        //   0 2 3
+        //   1 2 4
+        // While this kind of partitioning makes things somewhat more complicated, it
+        // ensures good utilization of all SMs for many kinds of shape and GPU
+        // configurations, while requiring as few slow global cross-threadblock
+        // reductions as possible.
+        using Dtype = ScalarType<scalar_t>;
+        using scalar_t2 = typename ScalarType<scalar_t>::scalar_t2;
+        using FragA = typename ScalarType<scalar_t>::FragA;
+        using FragB = typename ScalarType<scalar_t>::FragB;
+        using FragC = typename ScalarType<scalar_t>::FragC;
+        using FragS = typename ScalarType<scalar_t>::FragS;
+
+        constexpr int pack_factor = 32 / num_bits;
+
+        // int prob_m = *prob_m_ptr;
+        // const int thread_m_blocks = min(div_ceil(prob_m, 16), template_thread_m_blocks);
+        // constexpr int thread_m_blocks = template_thread_m_blocks;
+
+        // For larger GEMMs we run multiple batchsize 64 versions in parallel for a
+        // better partitioning with less reductions
+        int parallel = 1;
+        if (prob_m > 16 * thread_m_blocks) {
+            parallel = prob_m / (16 * thread_m_blocks);
+            prob_m = 16 * thread_m_blocks;
+        }
+
+        int k_tiles = prob_k / 16 / thread_k_blocks;
+        int n_tiles = prob_n / 16 / thread_n_blocks;
+        int iters = div_ceil(k_tiles * n_tiles * parallel, gridDim.x);
+
+        if constexpr (!has_act_order && group_blocks != -1) {
+            if (group_blocks >= thread_k_blocks) {
+                // Ensure that the number of tiles in each stripe is a multiple of the
+                // groupsize; this avoids an annoying special case where a stripe starts
+                // in the middle of group.
+                iters = (group_blocks / thread_k_blocks) *
+                    div_ceil(iters, (group_blocks / thread_k_blocks));
+            }
+        }
+
+        int slice_row = (iters * blockIdx.x) % k_tiles;
+        int slice_col_par = (iters * blockIdx.x) / k_tiles;
+        int slice_col = slice_col_par;
+        int slice_iters;  // number of threadblock tiles in the current slice
+        int slice_count =
+            0;          // total number of active threadblocks in the current slice
+        int slice_idx;  // index of threadblock in current slice; numbered bottom to
+        // top
+
+    // We can easily implement parallel problem execution by just remapping
+    // indices and advancing global pointers
+        if (slice_col_par >= n_tiles) {
+            A += (slice_col_par / n_tiles) * 16 * thread_m_blocks * prob_k / 8;
+            C += (slice_col_par / n_tiles) * 16 * thread_m_blocks * prob_n / 8;
+            locks += (slice_col_par / n_tiles) * n_tiles;
+            slice_col = slice_col_par % n_tiles;
+        }
+
+        // Compute all information about the current slice which is required for
+        // synchronization.
+        auto init_slice = [&]() {
+            slice_iters =
+                iters * (blockIdx.x + 1) - (k_tiles * slice_col_par + slice_row);
+            if (slice_iters < 0 || slice_col_par >= n_tiles * parallel) slice_iters = 0;
+            if (slice_iters == 0) return;
+            if (slice_row + slice_iters > k_tiles) slice_iters = k_tiles - slice_row;
+            slice_count = 1;
+            slice_idx = 0;
+            int col_first = iters * div_ceil(k_tiles * slice_col_par, iters);
+            if (col_first <= k_tiles * (slice_col_par + 1)) {
+                int col_off = col_first - k_tiles * slice_col_par;
+                slice_count = div_ceil(k_tiles - col_off, iters);
+                if (col_off > 0) slice_count++;
+                int delta_first = iters * blockIdx.x - col_first;
+                if (delta_first < 0 || (col_off == 0 && delta_first == 0))
+                    slice_idx = slice_count - 1;
+                else {
+                    slice_idx = slice_count - 1 - delta_first / iters;
+                    if (col_off > 0) slice_idx--;
+                }
+            }
+            if (slice_col == n_tiles) {
+                A += 16 * thread_m_blocks * prob_k / 8;
+                C += 16 * thread_m_blocks * prob_n / 8;
+                locks += n_tiles;
+                slice_col = 0;
+            }
+            };
+        init_slice();
+
+        // A sizes/strides
+
+        // stride of the A matrix in global memory
+        int a_gl_stride = prob_k / 8;
+        // stride of an A matrix tile in shared memory
+        constexpr int a_sh_stride = 16 * thread_k_blocks / 8;
+        // delta between subsequent A tiles in global memory
+        constexpr int a_gl_rd_delta_o = 16 * thread_k_blocks / 8;
+        // between subsequent accesses within a tile
+        int a_gl_rd_delta_i = a_gl_stride * (threads / a_gl_rd_delta_o);
+        // between shared memory writes
+        constexpr int a_sh_wr_delta = a_sh_stride * (threads / a_gl_rd_delta_o);
+        // between shared memory tile reads
+        constexpr int a_sh_rd_delta_o = 2 * ((threads / 32) / (thread_n_blocks / 4));
+        // within a shared memory tile
+        constexpr int a_sh_rd_delta_i = a_sh_stride * 16;
+        // overall size of a tile
+        constexpr int a_sh_stage = a_sh_stride * (16 * thread_m_blocks);
+        // number of shared write iterations for a tile
+        constexpr int a_sh_wr_iters = div_ceil(a_sh_stage, a_sh_wr_delta);
+
+        // B sizes/strides
+        int b_gl_stride = 16 * prob_n / (pack_factor * 4);
+        constexpr int b_sh_stride = ((thread_n_blocks * 16) * 16 / pack_factor) / 4;
+        constexpr int b_thread_vecs = num_bits == 4 ? 1 : 2;
+        constexpr int b_sh_stride_threads = b_sh_stride / b_thread_vecs;
+
+        int b_gl_rd_delta_o = b_gl_stride * thread_k_blocks;
+        int b_gl_rd_delta_i = b_gl_stride * (threads / b_sh_stride_threads);
+        constexpr int b_sh_wr_delta = threads * b_thread_vecs;
+        constexpr int b_sh_rd_delta = threads * b_thread_vecs;
+        constexpr int b_sh_stage = b_sh_stride * thread_k_blocks;
+        constexpr int b_sh_wr_iters = b_sh_stage / b_sh_wr_delta;
+
+        // Scale sizes/strides without act_order
+        int s_gl_stride = prob_n / 8;
+        constexpr int s_sh_stride = 16 * thread_n_blocks / 8;
+        constexpr int s_tb_groups =
+            !has_act_order && group_blocks != -1 && group_blocks < thread_k_blocks
+            ? thread_k_blocks / group_blocks
+            : 1;
+        constexpr int s_sh_stage = s_tb_groups * s_sh_stride;
+        int s_gl_rd_delta = s_gl_stride;
+
+        // Scale size/strides with act_order
+        constexpr int tb_k = 16 * thread_k_blocks;
+        constexpr int g_idx_stage = has_act_order ? (tb_k * sizeof(int)) / 16 : 0;
+        // constexpr int act_s_row_stride      = 1;
+        // int           act_s_col_stride      = act_s_row_stride * num_groups;
+        int act_s_col_stride = 1;
+        int act_s_col_warp_stride = act_s_col_stride * 8;
+        int tb_n_warps = thread_n_blocks / 4;
+        int act_s_col_tb_stride = act_s_col_warp_stride * tb_n_warps;
+
+        // Global A read index of current thread.
+        int a_gl_rd = a_gl_stride * (threadIdx.x / a_gl_rd_delta_o) +
+            (threadIdx.x % a_gl_rd_delta_o);
+        a_gl_rd += a_gl_rd_delta_o * slice_row;
+        // Shared write index of current thread.
+        int a_sh_wr = a_sh_stride * (threadIdx.x / a_gl_rd_delta_o) +
+            (threadIdx.x % a_gl_rd_delta_o);
+        // Shared read index.
+        int a_sh_rd =
+            a_sh_stride * ((threadIdx.x % 32) % 16) + (threadIdx.x % 32) / 16;
+        a_sh_rd += 2 * ((threadIdx.x / 32) / (thread_n_blocks / 4));
+
+        int b_gl_rd = b_gl_stride * (threadIdx.x / b_sh_stride_threads) +
+            (threadIdx.x % b_sh_stride_threads) * b_thread_vecs;
+        b_gl_rd += b_sh_stride * slice_col;
+        b_gl_rd += b_gl_rd_delta_o * slice_row;
+        int b_sh_wr = threadIdx.x * b_thread_vecs;
+        int b_sh_rd = threadIdx.x * b_thread_vecs;
+
+        // For act_order
+        constexpr int k_iter_size = tb_k / b_sh_wr_iters;
+        int slice_k_start = tb_k * slice_row;
+        int slice_k_finish = slice_k_start + tb_k * slice_iters;
+        int slice_k_start_shared_fetch = slice_k_start;
+        int slice_n_offset = act_s_col_tb_stride * slice_col;
+
+        // No act_order
+        int s_gl_rd;
+        if constexpr (!has_act_order) {
+            if constexpr (group_blocks == -1) {
+                s_gl_rd = s_sh_stride * slice_col + threadIdx.x;
+            }
+            else {
+                s_gl_rd = s_gl_stride * ((thread_k_blocks * slice_row) / group_blocks) +
+                    s_sh_stride * slice_col + threadIdx.x;
+            }
+        }
+        int s_sh_wr = threadIdx.x;
+        bool s_sh_wr_pred = threadIdx.x < s_sh_stride;
+
+        // We use a different scale layout for grouped and column-wise quantization as
+        // we scale a `half2` tile in column-major layout in the former and in
+        // row-major in the latter case.
+        int s_sh_rd;
+        if constexpr (group_blocks != -1)
+            s_sh_rd = 8 * ((threadIdx.x / 32) % (thread_n_blocks / 4)) +
+            (threadIdx.x % 32) / 4;
+        else
+            s_sh_rd = 8 * ((threadIdx.x / 32) % (thread_n_blocks / 4)) +
+            (threadIdx.x % 32) % 4;
+
+        // Precompute which thread should not read memory in which iterations; this is
+        // needed if there are more threads than required for a certain tilesize or
+        // when the batchsize is not a multiple of 16.
+        bool a_sh_wr_pred[a_sh_wr_iters];
+#pragma unroll
+        for (int i = 0; i < a_sh_wr_iters; i++) {
+            a_sh_wr_pred[i] = a_sh_wr_delta * i + a_sh_wr < a_sh_stride * prob_m;
+        }
+
+        // To ensure that writing and reading A tiles to/from shared memory, the
+        // latter in fragment format, is fully bank conflict free, we need to use a
+        // rather fancy XOR-based layout. The key here is that neither reads nor
+        // writes of the 16-byte `int4` blocks of 8 consecutive threads involve the
+        // same shared memory banks. Further, it seems (based on NSight-Compute) that
+        // each warp must also write a consecutive memory segment?
+        auto transform_a = [&](int i) {
+            int row = i / a_gl_rd_delta_o;
+            return a_gl_rd_delta_o * row + (i % a_gl_rd_delta_o) ^ row;
+            };
+        // Since the computation of this remapping is non-trivial and, due to our main
+        // loop unrolls, all shared memory accesses are static, we simply precompute
+        // both transformed reads and writes.
+        int a_sh_wr_trans[a_sh_wr_iters];
+#pragma unroll
+        for (int i = 0; i < a_sh_wr_iters; i++) {
+            a_sh_wr_trans[i] = transform_a(a_sh_wr_delta * i + a_sh_wr);
+        }
+        int a_sh_rd_trans[b_sh_wr_iters][thread_m_blocks];
+#pragma unroll
+        for (int i = 0; i < b_sh_wr_iters; i++) {
+#pragma unroll
+            for (int j = 0; j < thread_m_blocks; j++)
+            {
+                a_sh_rd_trans[i][j] =
+                    transform_a(a_sh_rd_delta_o * i + a_sh_rd_delta_i * j + a_sh_rd);
+            }
+        }
+
+        // Since B-accesses have non-constant stride they have to be computed at
+        // runtime; we break dependencies between subsequent accesses with a tile by
+        // maintining multiple pointers (we have enough registers), a tiny
+        // optimization.
+        const int4* B_ptr[b_sh_wr_iters];
+#pragma unroll
+        for (int i = 0; i < b_sh_wr_iters; i++)
+            B_ptr[i] = B + b_gl_rd_delta_i * i + b_gl_rd;
+
+        extern __shared__ int4 sh[];
+        // Shared memory storage for global fetch pipelines.
+        int4* sh_a = sh;
+        int4* sh_b = sh_a + (stages * a_sh_stage);
+        int4* sh_g_idx = sh_b + (stages * b_sh_stage);
+        int4* sh_s = sh_g_idx + (stages * g_idx_stage);
+
+        // Register storage for double buffer of shared memory reads.
+        FragA frag_a[2][thread_m_blocks];
+        I4 frag_b_quant[2][b_thread_vecs];
+        FragC frag_c[thread_m_blocks][4][2];
+        FragS frag_s[2][4];         // No act-order
+        FragS act_frag_s[2][4][4];  // For act-order
+
+        // Zero accumulators.
+        auto zero_accums = [&]() {
+#pragma unroll
+            for (int i = 0; i < thread_m_blocks * 4 * 2 * 4; i++)
+            {
+                reinterpret_cast<float*>(frag_c)[i] = 0;
+            }
+            };
+
+        int sh_first_group_id = -1;
+        int sh_num_groups = -1;
+        constexpr int sh_max_num_groups = 32;
+
+        auto fetch_scales_to_shared = [&](bool is_async, int first_group_id,
+            int last_group_id) {
+                sh_first_group_id = first_group_id;
+                sh_num_groups = last_group_id - first_group_id + 1;
+
+                if (sh_num_groups < sh_max_num_groups) {
+                    sh_num_groups = sh_max_num_groups;
+                }
+
+                if (sh_first_group_id + sh_num_groups > num_groups) {
+                    sh_num_groups = num_groups - sh_first_group_id;
+                }
+
+                int row_offset = first_group_id * s_gl_stride;
+
+                if (is_async) {
+                    for (int i = 0; i < sh_num_groups; i++) {
+                        if (threadIdx.x < s_sh_stride) {
+                            cp_async4_pred(&sh_s[(i * s_sh_stride) + threadIdx.x],
+                                &scales_ptr[row_offset + (i * s_gl_stride) +
+                                slice_n_offset + threadIdx.x]);
+                        }
+                    }
+                }
+                else {
+                    for (int i = 0; i < sh_num_groups; i++) {
+                        if (threadIdx.x < s_sh_stride) {
+                            sh_s[(i * s_sh_stride) + threadIdx.x] =
+                                scales_ptr[row_offset + (i * s_gl_stride) + slice_n_offset +
+                                threadIdx.x];
+                        }
+                    }
+                }
+            };
+        // Asynchronously fetch the next A, B and s tile from global to the next
+        // shared memory pipeline location.
+        auto fetch_to_shared = [&](int pipe, int a_off, bool pred = true) {
+            if (pred) {
+                int4* sh_a_stage = sh_a + a_sh_stage * pipe;
+#pragma unroll
+                for (int i = 0; i < a_sh_wr_iters; i++) {
+                    cp_async4_pred(
+                        &sh_a_stage[a_sh_wr_trans[i]],
+                        &A[a_gl_rd_delta_i * i + a_gl_rd + a_gl_rd_delta_o * a_off],
+                        a_sh_wr_pred[i]);
+                }
+                int4* sh_b_stage = sh_b + b_sh_stage * pipe;
+#pragma unroll
+                for (int i = 0; i < b_sh_wr_iters; i++) {
+#pragma unroll
+                    for (int j = 0; j < b_thread_vecs; j++) {
+                        cp_async4(&sh_b_stage[b_sh_wr_delta * i + b_sh_wr + j], B_ptr[i] + j);
+                    }
+
+                    B_ptr[i] += b_gl_rd_delta_o;
+                }
+
+                if constexpr (has_act_order) {
+                    // Fetch g_idx thread-block portion
+                    int full_pipe = a_off;
+                    int cur_k = slice_k_start_shared_fetch + tb_k * full_pipe;
+                    if (cur_k < prob_k && cur_k < slice_k_finish) {
+                        int4* sh_g_idx_stage = sh_g_idx + g_idx_stage * pipe;
+
+                        int4 const* cur_g_idx_stage_ptr =
+                            reinterpret_cast<int4 const*>(&g_idx[cur_k]);
+
+                        if (threadIdx.x < g_idx_stage) {
+                            cp_async4_pred(&sh_g_idx_stage[threadIdx.x],
+                                &cur_g_idx_stage_ptr[threadIdx.x]);
+                        }
+                    }
+                }
+                else {
+                    if constexpr (group_blocks != -1) {
+                        int4* sh_s_stage = sh_s + s_sh_stage * pipe;
+
+                        if constexpr (group_blocks >= thread_k_blocks) {
+                            // Only fetch scales if this tile starts a new group
+                            if (pipe % (group_blocks / thread_k_blocks) == 0) {
+                                if (s_sh_wr_pred) {
+                                    cp_async4(&sh_s_stage[s_sh_wr], &scales_ptr[s_gl_rd]);
+                                }
+                                s_gl_rd += s_gl_rd_delta;
+                            }
+                        }
+                        else {
+                            for (int i = 0; i < s_tb_groups; i++) {
+                                if (s_sh_wr_pred) {
+                                    cp_async4(&sh_s_stage[i * s_sh_stride + s_sh_wr],
+                                        &scales_ptr[s_gl_rd]);
+                                }
+                                s_gl_rd += s_gl_rd_delta;
+                            }
+                        }
+                    }
+                }
+            }
+            // Insert a fence even when we are winding down the pipeline to ensure that
+            // waiting is also correct at this point.
+            cp_async_fence();
+            };
+
+        // Wait until the next thread tile has been loaded to shared memory.
+        auto wait_for_stage = [&]() {
+            // We only have `stages - 2` active fetches since we are double buffering
+            // and can only issue the next fetch when it is guaranteed that the previous
+            // shared memory load is fully complete (as it may otherwise be
+            // overwritten).
+            cp_async_wait<stages - 2>();
+            __syncthreads();
+            };
+
+        // Load the next sub-tile from the current location in the shared memory pipe
+        // into the current register buffer.
+        auto fetch_to_registers = [&](int k, int pipe) {
+            int4* sh_a_stage = sh_a + a_sh_stage * pipe;
+#pragma unroll
+            for (int i = 0; i < thread_m_blocks; i++)
+            {
+                ldsm4<scalar_t>(frag_a[k % 2][i],
+                    &sh_a_stage[a_sh_rd_trans[k % b_sh_wr_iters][i]]);
+            }
+
+            int4* sh_b_stage = sh_b + b_sh_stage * pipe;
+
+#pragma unroll
+            for (int i = 0; i < b_thread_vecs; i++) {
+                frag_b_quant[k % 2][i] = *reinterpret_cast<I4*>(
+                    &sh_b_stage[b_sh_rd_delta * (k % b_sh_wr_iters) + b_sh_rd + i]);
+            }
+            };
+
+        bool is_same_group[stages];
+        int same_group_id[stages];
+
+        auto init_same_group = [&](int pipe) {
+            if constexpr (!has_act_order) {
+                is_same_group[pipe] = false;
+                same_group_id[pipe] = 0;
+                return;
+            }
+
+            int4* sh_g_idx_stage = sh_g_idx + g_idx_stage * pipe;
+            int* sh_g_idx_int_ptr = reinterpret_cast<int*>(sh_g_idx_stage);
+
+            int group_id_1 = sh_g_idx_int_ptr[0];
+            int group_id_2 = sh_g_idx_int_ptr[tb_k - 1];
+
+            is_same_group[pipe] = group_id_1 == group_id_2;
+            same_group_id[pipe] = group_id_1;
+            };
+
+        auto fetch_scales_to_registers = [&](int k, int full_pipe) {
+            int pipe = full_pipe % stages;
+
+            if constexpr (!has_act_order) {
+                // No act-order case
+                if constexpr (group_blocks != -1) {
+                    if constexpr (group_blocks >= thread_k_blocks) {
+                        int4* sh_s_stage =
+                            sh_s + s_sh_stage * ((group_blocks / thread_k_blocks) *
+                                (pipe / (group_blocks / thread_k_blocks)));
+                        reinterpret_cast<int4*>(&frag_s[k % 2])[0] = sh_s_stage[s_sh_rd];
+                    }
+                    else {
+                        int warp_id = threadIdx.x / 32;
+                        int n_warps = thread_n_blocks / 4;
+
+                        int warp_row = warp_id / n_warps;
+
+                        int cur_k = warp_row * 16;
+                        cur_k += k_iter_size * (k % b_sh_wr_iters);
+
+                        int k_blocks = cur_k / 16;
+                        int cur_group_id = k_blocks / group_blocks;
+
+                        int4* sh_s_stage = sh_s + s_sh_stage * pipe;
+
+                        reinterpret_cast<int4*>(&frag_s[k % 2])[0] =
+                            sh_s_stage[s_sh_rd + cur_group_id * s_sh_stride];
+                    }
+                }
+
+                return;
+            }
+
+            // Act-order case
+
+            // Determine K of the "current" thread-block
+            int cur_k = slice_k_start + tb_k * full_pipe;
+            if (cur_k >= prob_k || cur_k >= slice_k_finish) {
+                return;
+            }
+
+            // Reset (to current thread-block) since we read g_idx portion from the
+            // shared memory
+            cur_k = 0;
+
+            // Progress to current iteration
+            cur_k += k_iter_size * (k % b_sh_wr_iters);
+
+            // Determine "position" inside the thread-block (based on warp and
+            // thread-id)
+            int warp_id = threadIdx.x / 32;
+            int n_warps =
+                thread_n_blocks / 4;  // Each warp processes 4 16-size tiles over N
+
+            int warp_row = warp_id / n_warps;
+            int warp_col = warp_id % n_warps;
+
+            cur_k += warp_row * 16;
+
+            int th_id = threadIdx.x % 32;
+            cur_k += (th_id % 4) * 2;  // Due to tensor-core layout for fp16 B matrix
+
+            int s_col_shift =
+                /*slice_n_offset +*/ (act_s_col_warp_stride * warp_col) +
+                (th_id / 4) * act_s_col_stride;
+
+            if (is_same_group[pipe]) {
+                if (k % 2 == 0) {
+                    *(reinterpret_cast<int4*>(&(act_frag_s[k % 2][0][0]))) =
+                        sh_s[(same_group_id[pipe] - sh_first_group_id) * s_sh_stride +
+                        s_col_shift];
+                }
+                else {
+                    *(reinterpret_cast<int4*>(&(act_frag_s[k % 2][0][0]))) =
+                        *(reinterpret_cast<int4*>(&(act_frag_s[(k - 1) % 2][0][0])));
+                }
+
+                for (int i = 1; i < 4; i++) {
+                    *(reinterpret_cast<int4*>(&(act_frag_s[k % 2][i][0]))) =
+                        *(reinterpret_cast<int4*>(&(act_frag_s[k % 2][0][0])));
+                }
+                return;
+            }
+
+            int4* sh_g_idx_stage = sh_g_idx + g_idx_stage * pipe;
+            int* sh_g_idx_int_ptr = reinterpret_cast<int*>(sh_g_idx_stage);
+
+            constexpr int k_frag_offsets[4] = { 0, 1, 8,
+                                               9 };  // Tensor core offsets per thread
+
+#pragma unroll
+            for (int i = 0; i < 4; i++) {
+                int actual_k = cur_k + k_frag_offsets[i];
+
+                int group_id = sh_g_idx_int_ptr[actual_k];
+                int rel_group_id = group_id - sh_first_group_id;
+
+                *(reinterpret_cast<int4*>(&(act_frag_s[k % 2][i][0]))) =
+                    sh_s[rel_group_id * s_sh_stride + s_col_shift];
+            }
+            };
+
+        // Execute the actual tensor core matmul of a sub-tile.
+        auto matmul = [&](int k) {
+            // We have the m dimension as the inner loop in order to encourage overlapping
+            // dequantization and matmul operations.
+#pragma unroll
+            for (int j = 0; j < 4; j++) {
+                FragB frag_b0;
+                FragB frag_b1;
+                if constexpr (num_bits == 4) {
+                    int b_quant = frag_b_quant[k % 2][0][j];
+                    int b_quant_shift = b_quant >> 8;
+
+                    frag_b0 = dequant_4bit<scalar_t>(b_quant);
+                    frag_b1 = dequant_4bit<scalar_t>(b_quant_shift);
+
+                }
+                else {
+                    int* frag_b_quant_ptr = reinterpret_cast<int*>(frag_b_quant[k % 2]);
+                    int b_quant_0 = frag_b_quant_ptr[j * 2 + 0];
+                    int b_quant_1 = frag_b_quant_ptr[j * 2 + 1];
+
+                    frag_b0 = dequant_8bit<scalar_t>(b_quant_0);
+                    frag_b1 = dequant_8bit<scalar_t>(b_quant_1);
+                }
+
+                // Apply scale to frag_b0
+                if constexpr (has_act_order) {
+                    scale4<scalar_t>(frag_b0, act_frag_s[k % 2][0][j],
+                        act_frag_s[k % 2][1][j], act_frag_s[k % 2][2][j],
+                        act_frag_s[k % 2][3][j], 0);
+                }
+                else {
+                    if constexpr (group_blocks != -1) {
+                        scale<scalar_t>(frag_b0, frag_s[k % 2][j], 0);
+                    }
+                }
+
+                // Apply scale to frag_b1
+                if constexpr (has_act_order) {
+                    scale4<scalar_t>(frag_b1, act_frag_s[k % 2][0][j],
+                        act_frag_s[k % 2][1][j], act_frag_s[k % 2][2][j],
+                        act_frag_s[k % 2][3][j], 1);
+
+                }
+                else {
+                    if constexpr (group_blocks != -1) {
+                        scale<scalar_t>(frag_b1, frag_s[k % 2][j], 1);
+                    }
+                }
+
+#pragma unroll
+                for (int i = 0; i < thread_m_blocks; i++) {
+                    mma<scalar_t>(frag_a[k % 2][i], frag_b0, frag_c[i][j][0]);
+                    mma<scalar_t>(frag_a[k % 2][i], frag_b1, frag_c[i][j][1]);
+                }
+            }
+            };
+
+        // Since we slice across the k dimension of a tile in order to increase the
+        // number of warps while keeping the n dimension of a tile reasonable, we have
+        // multiple warps that accumulate their partial sums of the same output
+        // location; which we have to reduce over in the end. We do in shared memory.
+        auto thread_block_reduce = [&]() {
+            constexpr int red_off = threads / b_sh_stride_threads / 2;
+            if (red_off >= 1) {
+                int red_idx = threadIdx.x / b_sh_stride_threads;
+                constexpr int red_sh_stride = b_sh_stride_threads * 4 * 2;
+                constexpr int red_sh_delta = b_sh_stride_threads;
+                int red_sh_rd = red_sh_stride * (threadIdx.x / b_sh_stride_threads) +
+                    (threadIdx.x % b_sh_stride_threads);
+
+                // Parallel logarithmic shared memory reduction. We make sure to avoid any
+                // unnecessary read or write iterations, e.g., for two warps we write only
+                // once by warp 1 and read only once by warp 0.
+
+#pragma unroll
+                for (int m_block = 0; m_block < thread_m_blocks; m_block++) {
+#pragma unroll
+                    for (int i = red_off; i > 0; i /= 2) {
+                        if (i <= red_idx && red_idx < 2 * i) {
+#pragma unroll
+                            for (int j = 0; j < 4 * 2; j++) {
+                                int red_sh_wr =
+                                    red_sh_delta * j + (red_sh_rd - red_sh_stride * i);
+                                if (i < red_off) {
+                                    float* c_rd =
+                                        reinterpret_cast<float*>(&sh[red_sh_delta * j + red_sh_rd]);
+                                    float* c_wr = reinterpret_cast<float*>(&sh[red_sh_wr]);
+#pragma unroll
+                                    for (int k = 0; k < 4; k++)
+                                        reinterpret_cast<FragC*>(frag_c)[4 * 2 * m_block + j][k] +=
+                                        c_rd[k] + c_wr[k];
+                                }
+                                sh[red_sh_wr] =
+                                    reinterpret_cast<int4*>(&frag_c)[4 * 2 * m_block + j];
+                            }
+                        }
+                        __syncthreads();
+                    }
+                    if (red_idx == 0) {
+#pragma unroll
+                        for (int i = 0; i < 4 * 2; i++) {
+                            float* c_rd =
+                                reinterpret_cast<float*>(&sh[red_sh_delta * i + red_sh_rd]);
+#pragma unroll
+                            for (int j = 0; j < 4; j++)
+                                reinterpret_cast<FragC*>(frag_c)[4 * 2 * m_block + i][j] +=
+                                c_rd[j];
+                        }
+                    }
+                    __syncthreads();
+                }
+            }
+            };
+
+        // Since multiple threadblocks may process parts of the same column slice, we
+        // finally have to globally reduce over the results. As the striped
+        // partitioning minimizes the number of such reductions and our outputs are
+        // usually rather small, we perform this reduction serially in L2 cache.
+        auto global_reduce = [&](bool first = false, bool last = false) {
+            // We are very careful here to reduce directly in the output buffer to
+            // maximize L2 cache utilization in this step. To do this, we write out
+            // results in FP16 (but still reduce with FP32 compute).
+            constexpr int active_threads = 32 * thread_n_blocks / 4;
+            if (threadIdx.x < active_threads) {
+                int c_gl_stride = prob_n / 8;
+                int c_gl_wr_delta_o = 8 * c_gl_stride;
+                int c_gl_wr_delta_i = 4 * (active_threads / 32);
+                int c_gl_wr = c_gl_stride * ((threadIdx.x % 32) / 4) +
+                    4 * (threadIdx.x / 32) + threadIdx.x % 4;
+                c_gl_wr += (2 * thread_n_blocks) * slice_col;
+                constexpr int c_sh_wr_delta = active_threads;
+                int c_sh_wr = threadIdx.x;
+
+                int row = (threadIdx.x % 32) / 4;
+
+                if (!first) {
+                    // Interestingly, doing direct global accesses here really seems to mess up
+                    // the compiler and lead to slowdowns, hence we also use async-copies even
+                    // though these fetches are not actually asynchronous.
+#pragma unroll
+                    for (int i = 0; i < thread_m_blocks * 4; i++) {
+                        cp_async4_pred(
+                            &sh[c_sh_wr + c_sh_wr_delta * i],
+                            &C[c_gl_wr + c_gl_wr_delta_o * (i / 2) +
+                            c_gl_wr_delta_i * (i % 2)],
+                            i < (thread_m_blocks - 1) * 4 || 8 * (i / 2) + row < prob_m);
+                    }
+                    cp_async_fence();
+                    cp_async_wait<0>();
+                }
+
+#pragma unroll
+                for (int i = 0; i < thread_m_blocks * 4; i++) {
+                    if (i < (thread_m_blocks - 1) * 4 || 8 * (i / 2) + row < prob_m) {
+                        if (!first) {
+                            int4 c_red = sh[c_sh_wr + i * c_sh_wr_delta];
+#pragma unroll
+                            for (int j = 0; j < 2 * 4; j++) {
+                                reinterpret_cast<float*>(
+                                    &frag_c)[4 * 2 * 4 * (i / 4) + 4 * j + (i % 4)] +=
+                                    Dtype::num2float(reinterpret_cast<scalar_t*>(&c_red)[j]);
+                            }
+                        }
+                        if (!last) {
+                            int4 c;
+#pragma unroll
+                            for (int j = 0; j < 2 * 4; j++) {
+                                reinterpret_cast<scalar_t*>(&c)[j] =
+                                    Dtype::float2num(reinterpret_cast<float*>(
+                                        &frag_c)[4 * 2 * 4 * (i / 4) + 4 * j + (i % 4)]);
+                            }
+                            C[c_gl_wr + c_gl_wr_delta_o * (i / 2) + c_gl_wr_delta_i * (i % 2)] =
+                                c;
+                        }
+                    }
+                }
+            }
+            };
+
+        // Write out the reduce final result in the correct layout. We only actually
+        // reshuffle matrix fragments in this step, the reduction above is performed
+        // in fragment layout.
+        auto write_result = [&]() {
+            int c_gl_stride = prob_n / 8;
+            constexpr int c_sh_stride = 2 * thread_n_blocks + 1;
+            int c_gl_wr_delta = c_gl_stride * (threads / (2 * thread_n_blocks));
+            constexpr int c_sh_rd_delta =
+                c_sh_stride * (threads / (2 * thread_n_blocks));
+
+            int c_gl_wr = c_gl_stride * (threadIdx.x / (2 * thread_n_blocks)) +
+                (threadIdx.x % (2 * thread_n_blocks));
+            c_gl_wr += (2 * thread_n_blocks) * slice_col;
+            int c_sh_wr =
+                (4 * c_sh_stride) * ((threadIdx.x % 32) / 4) + (threadIdx.x % 32) % 4;
+            c_sh_wr += 32 * (threadIdx.x / 32);
+            int c_sh_rd = c_sh_stride * (threadIdx.x / (2 * thread_n_blocks)) +
+                (threadIdx.x % (2 * thread_n_blocks));
+
+            int c_gl_wr_end = c_gl_stride * prob_m;
+
+            // We first reorder in shared memory to guarantee the most efficient final
+            // global write patterns
+            auto write = [&](int idx, float c0, float c1, FragS& s) {
+                scalar_t2 res =
+                    Dtype::nums2num2(Dtype::float2num(c0), Dtype::float2num(c1));
+
+                // For per-column quantization we finally apply the scale here (only for
+                // 4-bit)
+                if constexpr (!has_act_order && group_blocks == -1 && num_bits == 4) {
+                    res = __hmul2(res, s[0]);
+                }
+
+                ((scalar_t2*)sh)[idx] = res;
+                };
+
+            if (threadIdx.x / 32 < thread_n_blocks / 4) {
+#pragma unroll
+                for (int i = 0; i < thread_m_blocks; i++) {
+#pragma unroll
+                    for (int j = 0; j < 4; j++) {
+                        int wr = c_sh_wr + 8 * j;
+                        write(wr + (4 * c_sh_stride) * 0 + 0, frag_c[i][j][0][0],
+                            frag_c[i][j][0][1], frag_s[j / 2][2 * (j % 2) + 0]);
+                        write(wr + (4 * c_sh_stride) * 8 + 0, frag_c[i][j][0][2],
+                            frag_c[i][j][0][3], frag_s[j / 2][2 * (j % 2) + 0]);
+                        write(wr + (4 * c_sh_stride) * 0 + 4, frag_c[i][j][1][0],
+                            frag_c[i][j][1][1], frag_s[j / 2][2 * (j % 2) + 1]);
+                        write(wr + (4 * c_sh_stride) * 8 + 4, frag_c[i][j][1][2],
+                            frag_c[i][j][1][3], frag_s[j / 2][2 * (j % 2) + 1]);
+                    }
+                    c_sh_wr += 16 * (4 * c_sh_stride);
+                }
+            }
+            __syncthreads();
+
+#pragma unroll
+            for (int i = 0;
+                i < div_ceil(16 * thread_m_blocks, threads / (2 * thread_n_blocks));
+                i++) {
+                if (c_gl_wr < c_gl_wr_end) {
+                    C[c_gl_wr] = sh[c_sh_rd];
+                    c_gl_wr += c_gl_wr_delta;
+                    c_sh_rd += c_sh_rd_delta;
+                }
+            }
+            };
+
+        // Start global fetch and register load pipelines.
+        auto start_pipes = [&]() {
+
+#pragma unroll
+            for (int i = 0; i < stages - 1; i++) {
+                if (has_act_order && i == 0) {
+                    int last_g_idx = slice_k_start + stages * tb_k * 2;
+                    if (last_g_idx >= prob_k) {
+                        last_g_idx = prob_k - 1;
+                    }
+                    fetch_scales_to_shared(true, g_idx[slice_k_start], g_idx[last_g_idx]);
+                }
+                fetch_to_shared(i, i, i < slice_iters);
+            }
+
+            zero_accums();
+            wait_for_stage();
+            init_same_group(0);
+            fetch_to_registers(0, 0);
+            fetch_scales_to_registers(0, 0);
+            a_gl_rd += a_gl_rd_delta_o * (stages - 1);
+            slice_k_start_shared_fetch += tb_k * (stages - 1);
+            };
+        if (slice_iters) {
+            start_pipes();
+        }
+
+        // Main loop.
+        while (slice_iters) {
+            // We unroll over both the global fetch and the register load pipeline to
+            // ensure all shared memory accesses are static. Note that both pipelines
+            // have even length meaning that the next iteration will always start at
+            // index 0.
+
+#pragma unroll
+            for (int pipe = 0; pipe < stages;) {
+#pragma unroll
+                for (int k = 0; k < b_sh_wr_iters; k++) {
+                    fetch_to_registers(k + 1, pipe % stages);
+                    fetch_scales_to_registers(k + 1, pipe);
+                    if (k == b_sh_wr_iters - 2) {
+                        fetch_to_shared((pipe + stages - 1) % stages, pipe,
+                            slice_iters >= stages);
+                        pipe++;
+                        wait_for_stage();
+                        init_same_group(pipe % stages);
+                    }
+                    matmul(k);
+                }
+                slice_iters--;
+                if (slice_iters == 0) {
+                    break;
+                }
+            }
+
+            a_gl_rd += a_gl_rd_delta_o * stages;
+            slice_k_start += tb_k * stages;
+            slice_k_start_shared_fetch += tb_k * stages;
+
+            if constexpr (has_act_order) {
+                int first_group_id = g_idx[slice_k_start];
+                int last_g_idx = slice_k_start + stages * tb_k * 2;
+                if (last_g_idx >= prob_k) {
+                    last_g_idx = prob_k - 1;
+                }
+                int last_group_id = g_idx[last_g_idx];
+                if (last_group_id >= sh_first_group_id + sh_num_groups) {
+                    fetch_scales_to_shared(false, first_group_id, last_group_id);
+                    __syncthreads();
+                }
+            }
+
+            // Process results and, if necessary, proceed to the next column slice.
+            // While this pattern may not be the most readable, other ways of writing
+            // the loop seemed to noticeably worse performance after compilation.
+            if (slice_iters == 0) {
+                cp_async_wait<0>();
+                bool last = slice_idx == slice_count - 1;
+                // For per-column scales, we only fetch them here in the final step before
+                // write-out
+                if constexpr (!has_act_order && group_blocks == -1) {
+                    if constexpr (num_bits == 8) {
+                        if (s_sh_wr_pred) {
+                            cp_async4(&sh_s[s_sh_wr], &scales_ptr[s_gl_rd]);
+                        }
+                        cp_async_fence();
+                    }
+                    else {
+                        if (last) {
+                            if (s_sh_wr_pred) {
+                                cp_async4(&sh_s[s_sh_wr], &scales_ptr[s_gl_rd]);
+                            }
+                            cp_async_fence();
+                        }
+                    }
+                }
+
+                thread_block_reduce();
+                if constexpr (!has_act_order && group_blocks == -1) {
+                    if constexpr (num_bits == 8) {
+                        cp_async_wait<0>();
+                        __syncthreads();
+                        if (threadIdx.x / 32 < thread_n_blocks / 4) {
+                            reinterpret_cast<int4*>(&frag_s)[0] = sh_s[s_sh_rd + 0];
+                            reinterpret_cast<int4*>(&frag_s)[1] = sh_s[s_sh_rd + 4];
+                        }
+
+                    }
+                    else {
+                        if (last) {
+                            cp_async_wait<0>();
+                            __syncthreads();
+                            if (threadIdx.x / 32 < thread_n_blocks / 4) {
+                                reinterpret_cast<int4*>(&frag_s)[0] = sh_s[s_sh_rd + 0];
+                                reinterpret_cast<int4*>(&frag_s)[1] = sh_s[s_sh_rd + 4];
+                            }
+                        }
+                    }
+                }
+
+                // For 8-bit channelwise, we apply the scale before the global reduction
+                // that converts the fp32 results to fp16 (so that we avoid possible
+                // overflow in fp16)
+                if constexpr (!has_act_order && group_blocks == -1 && num_bits == 8) {
+                    if (threadIdx.x / 32 < thread_n_blocks / 4) {
+#pragma unroll
+                        for (int i = 0; i < thread_m_blocks; i++) {
+#pragma unroll
+                            for (int j = 0; j < 4; j++) {
+                                scale_float<scalar_t>(
+                                    reinterpret_cast<float*>(&frag_c[i][j][0][0]),
+                                    frag_s[j / 2][2 * (j % 2) + 0]);
+                                scale_float<scalar_t>(
+                                    reinterpret_cast<float*>(&frag_c[i][j][0][2]),
+                                    frag_s[j / 2][2 * (j % 2) + 0]);
+
+                                scale_float<scalar_t>(
+                                    reinterpret_cast<float*>(&frag_c[i][j][1][0]),
+                                    frag_s[j / 2][2 * (j % 2) + 1]);
+                                scale_float<scalar_t>(
+                                    reinterpret_cast<float*>(&frag_c[i][j][1][2]),
+                                    frag_s[j / 2][2 * (j % 2) + 1]);
+                            }
+                        }
+                    }
+                }
+
+                if (slice_count > 1) {  // only globally reduce if there is more than one
+                    // block in a slice
+                    barrier_acquire(&locks[slice_col], slice_idx);
+                    global_reduce(slice_idx == 0, last);
+                    barrier_release(&locks[slice_col], last);
+                }
+                if (last)  // only the last block in a slice actually writes the result
+                    write_result();
+                slice_row = 0;
+                slice_col_par++;
+                slice_col++;
+                init_slice();
+                if (slice_iters) {
+                    a_gl_rd = a_gl_stride * (threadIdx.x / a_gl_rd_delta_o) +
+                        (threadIdx.x % a_gl_rd_delta_o);
+#pragma unroll
+                    for (int i = 0; i < b_sh_wr_iters; i++)
+                        B_ptr[i] += b_sh_stride - b_gl_rd_delta_o * k_tiles;
+                    if (slice_col == 0) {
+#pragma unroll
+                        for (int i = 0; i < b_sh_wr_iters; i++) B_ptr[i] -= b_gl_stride;
+                    }
+
+                    // Update slice k/n for scales loading
+                    if constexpr (has_act_order) {
+                        slice_k_start = tb_k * slice_row;
+                        slice_k_finish = slice_k_start + tb_k * slice_iters;
+                        slice_k_start_shared_fetch = slice_k_start;
+                        slice_n_offset = act_s_col_tb_stride * slice_col;
+
+                    }
+                    else {
+                        s_gl_rd = s_sh_stride * slice_col + threadIdx.x;
+                    }
+
+                    start_pipes();
+                }
+            }
+        }
+    }
+
+    template <typename scalar_t,         // compute dtype, half or nv_float16
+        const int num_bits,        // number of bits used for weights
+        const int threads,         // number of threads in a threadblock
+        const int template_thread_m_blocks, // number of 16x16 blocks in the m
+        // dimension (batchsize) of the
+        // threadblock
+        const int thread_n_blocks, // same for n dimension (output)
+        const int thread_k_blocks, // same for k dimension (reduction)
+        const int stages, // number of stages for the async global->shared
+        // fetch pipeline
+        const bool has_act_order,   // whether act_order is enabled
+        const int group_blocks = -1 // number of consecutive 16x16 blocks
+        // with a separate quantization scale
+    >
+    __global__ void
+        Marlin_wrapper(const int4* __restrict__ A, // fp16 input matrix of shape mxk
+            const int4* __restrict__ B, // 4bit quantized weight matrix of shape kxn
+            int4* __restrict__ C,       // fp16 output buffer of shape mxn
+            const int4* __restrict__ scales_ptr, // fp16 quantization scales of shape
+            // (k/groupsize)xn
+            const int* __restrict__ g_idx, // int32 group indices of shape k
+            int num_groups, // number of scale groups per output channel
+            const int* __restrict__ prob_m_ptr,     // batch dimension m
+            int prob_n,     // output dimension n
+            int prob_k,     // reduction dimension k
+            int* locks      // extra global storage for barrier synchronization
+        ) {
+        int prob_m = *prob_m_ptr;
+        const int thread_m_blocks = min(div_ceil(prob_m, 16), template_thread_m_blocks);
+        if(prob_m > 16 * thread_m_blocks)
+            prob_m = (16 * thread_m_blocks) * div_ceil(prob_m, (16 * thread_m_blocks));
+        /*if (blockIdx.x == 0 && threadIdx.x == 0)
+            printf("marlin prob_m %d\n", prob_m);*/
+        if (thread_m_blocks == 1) {
+            Marlin<scalar_t, num_bits, threads, 1,
+                thread_n_blocks, thread_k_blocks, stages, has_act_order,
+                group_blocks>(
+                    A, B, C, scales_ptr, g_idx, num_groups, prob_m, prob_n,
+                    prob_k, locks);
+        }
+        else if (thread_m_blocks == 2) {
+            Marlin<scalar_t, num_bits, threads, 2,
+                thread_n_blocks, thread_k_blocks, stages, has_act_order,
+                group_blocks>(
+                    A, B, C, scales_ptr, g_idx, num_groups, prob_m, prob_n,
+                    prob_k, locks);
+        }
+        else if (thread_m_blocks == 3) {
+            Marlin<scalar_t, num_bits, threads, 3,
+                thread_n_blocks, thread_k_blocks, stages, has_act_order,
+                group_blocks>(
+                    A, B, C, scales_ptr, g_idx, num_groups, prob_m, prob_n,
+                    prob_k, locks);
+        }
+        else if (thread_m_blocks == 4) {
+            Marlin<scalar_t, num_bits, threads, 4,
+                thread_n_blocks, thread_k_blocks, stages, has_act_order,
+                group_blocks>(
+                    A, B, C, scales_ptr, g_idx, num_groups, prob_m, prob_n,
+                    prob_k, locks);
+        }
+    }
+
+#define __CALL_IF(NUM_BITS, THREAD_M_BLOCKS, THREAD_N_BLOCKS, THREAD_K_BLOCKS, \
+                  HAS_ACT_ORDER, GROUP_BLOCKS, NUM_THREADS)                    \
+    else if (num_bits == NUM_BITS && thread_m_blocks == THREAD_M_BLOCKS &&     \
+             thread_n_blocks == THREAD_N_BLOCKS &&                             \
+             thread_k_blocks == THREAD_K_BLOCKS &&                             \
+             has_act_order == HAS_ACT_ORDER && group_blocks == GROUP_BLOCKS && \
+             num_threads == NUM_THREADS) {                                     \
+        cudaFuncSetAttribute(                                                  \
+            Marlin_wrapper<scalar_t, NUM_BITS, NUM_THREADS, THREAD_M_BLOCKS,           \
+                   THREAD_N_BLOCKS, THREAD_K_BLOCKS, pipe_stages,              \
+                   HAS_ACT_ORDER, GROUP_BLOCKS>,                               \
+            cudaFuncAttributeMaxDynamicSharedMemorySize, max_shared_mem);      \
+        Marlin_wrapper<scalar_t, NUM_BITS, NUM_THREADS, THREAD_M_BLOCKS,               \
+               THREAD_N_BLOCKS, THREAD_K_BLOCKS, pipe_stages, HAS_ACT_ORDER,   \
+               GROUP_BLOCKS><<<blocks, NUM_THREADS, max_shared_mem, stream>>>( \
+            A_ptr, B_ptr, C_ptr, s_ptr, g_idx_ptr, num_groups, prob_m_ptr, prob_n, \
+            prob_k, locks);                                                    \
+    }
+
+    typedef struct {
+        int thread_k;
+        int thread_n;
+        int num_threads;
+    } thread_config_t;
+
+    typedef struct {
+        int max_m_blocks;
+        thread_config_t tb_cfg;
+    } exec_config_t;
+
+    thread_config_t small_batch_thread_configs[] = {
+        // Ordered by priority
+
+        // thread_k, thread_n, num_threads
+        {128, 128, 256},
+        {64, 128, 128},
+        {128, 64, 128},
+    };
+
+    thread_config_t large_batch_thread_configs[] = {
+        // Ordered by priority
+
+        // thread_k, thread_n, num_threads
+        {64, 256, 256},
+        // {128, 128, 256},
+        {64, 128, 128},
+        {128, 64, 128},
+
+    };
+
+    int get_scales_cache_size(thread_config_t const& th_config, int prob_m,
+        int prob_n, int prob_k, int num_bits, int group_size,
+        bool has_act_order, bool is_k_full) {
+        bool cache_scales_chunk = has_act_order && !is_k_full;
+
+        int tb_n = th_config.thread_n;
+        int tb_k = th_config.thread_k;
+
+        // Get max scale groups per thread-block
+        int tb_groups;
+        if (group_size == -1) {
+            tb_groups = 1;
+        }
+        else if (group_size == 0) {
+            tb_groups = div_ceil(tb_k, 32); // Worst case is 32 group size
+        }
+        else {
+            tb_groups = div_ceil(tb_k, group_size);
+        }
+
+        if (cache_scales_chunk) {
+            int load_groups =
+                tb_groups * pipe_stages * 2; // Chunk size is 2x pipeline over dim K
+            load_groups = max(load_groups, 32); // We load at least 32 scale groups
+            return load_groups * tb_n * 2;
+
+        }
+        else {
+            int tb_scales = tb_groups * tb_n * 2;
+
+            return tb_scales * pipe_stages;
+        }
+    }
+
+    bool is_valid_cache_size(thread_config_t const& th_config, int max_m_blocks,
+        int prob_m, int prob_n, int prob_k, int num_bits,
+        int scales_cache_size, int max_shared_mem) {
+        int pack_factor = 32 / num_bits;
+
+        // Get B size
+        int tb_k = th_config.thread_k;
+        int tb_n = th_config.thread_n;
+
+        int b_size = (tb_k * tb_n / pack_factor) * 4;
+
+        // Get A size
+        int m_blocks = div_ceil(prob_m, 16);
+        int tb_max_m = 16;
+
+        // zbx: too ugly
+        // origin
+        /*while (true) {
+          if (m_blocks >= max_m_blocks) {
+            tb_max_m *= max_m_blocks;
+            break;
+          }
+
+          max_m_blocks--;
+          if (max_m_blocks == 0) {
+            TORCH_CHECK(false, "Unexpected m_blocks = ", m_blocks);
+          }
+        }*/
+        // refactor
+        tb_max_m *= std::min(m_blocks, max_m_blocks);
+
+        int a_size = (tb_max_m * tb_k) * 2;
+
+        float pipe_size = (a_size + b_size) * pipe_stages;
+
+        TORCH_CHECK(max_shared_mem / 2 > scales_cache_size); // Sanity
+        return pipe_size < 0.95f * (max_shared_mem - scales_cache_size);
+    }
+
+    bool is_valid_config(thread_config_t const& th_config, int max_m_blocks,
+        int prob_m, int prob_n, int prob_k, int num_bits,
+        int group_size, bool has_act_order, bool is_k_full,
+        int max_shared_mem) {
+        // Sanity
+        if (th_config.thread_k == -1 || th_config.thread_n == -1 ||
+            th_config.num_threads == -1) {
+            return false;
+        }
+
+        // Verify K/N are divisible by thread K/N
+        if (prob_k % th_config.thread_k != 0 || prob_n % th_config.thread_n != 0) {
+            return false;
+        }
+
+        // Verify min for thread K/N
+        if (th_config.thread_n < min_thread_n ||
+            th_config.thread_k < min_thread_k) {
+            return false;
+        }
+
+        // num_threads must be at least 128 (= 4 warps)
+        if (th_config.num_threads < 128) {
+            return false;
+        }
+
+        //  Determine cache for scales
+        int scales_cache_size =
+            get_scales_cache_size(th_config, prob_m, prob_n, prob_k, num_bits,
+                group_size, has_act_order, is_k_full);
+
+        // Check that pipeline fits into cache
+        if (!is_valid_cache_size(th_config, max_m_blocks, prob_m, prob_n, prob_k,
+            num_bits, scales_cache_size, max_shared_mem)) {
+            return false;
+        }
+
+        return true;
+    }
+
+    exec_config_t determine_thread_config(int prob_m, int prob_n, int prob_k,
+        int num_bits, int group_size,
+        bool has_act_order, bool is_k_full,
+        int max_shared_mem) {
+        int max_m_blocks = 4;
+        while (max_m_blocks > 0) {
+            if (prob_m <= 16) {
+                for (auto th_config : small_batch_thread_configs) {
+                    if (is_valid_config(th_config, max_m_blocks, prob_m, prob_n,
+                        prob_k, num_bits, group_size, has_act_order,
+                        is_k_full, max_shared_mem)) {
+                        return exec_config_t{ max_m_blocks, th_config };
+                    }
+                }
+            }
+            else {
+                for (auto th_config : large_batch_thread_configs) {
+                    if (is_valid_config(th_config, max_m_blocks, prob_m, prob_n,
+                        prob_k, num_bits, group_size, has_act_order,
+                        is_k_full, max_shared_mem)) {
+                        return exec_config_t{ max_m_blocks, th_config };
+                    }
+                }
+            }
+
+            max_m_blocks--; // Process less M blocks per invocation to reduce cache
+            // usage
+        }
+
+        return exec_config_t{ 0, {-1, -1, -1} };
+    }
+
+#define CALL_IF(NUM_BITS, N_BLOCKS, K_BLOCKS, NUM_THREADS)                     \
+    __CALL_IF(NUM_BITS, 1, N_BLOCKS, K_BLOCKS, false, 4, NUM_THREADS)          \
+    __CALL_IF(NUM_BITS, 2, N_BLOCKS, K_BLOCKS, false, 4, NUM_THREADS)          \
+    __CALL_IF(NUM_BITS, 3, N_BLOCKS, K_BLOCKS, false, 4, NUM_THREADS)          \
+    __CALL_IF(NUM_BITS, 4, N_BLOCKS, K_BLOCKS, false, 4, NUM_THREADS)          \
+    __CALL_IF(NUM_BITS, 1, N_BLOCKS, K_BLOCKS, false, 8, NUM_THREADS)          \
+    __CALL_IF(NUM_BITS, 2, N_BLOCKS, K_BLOCKS, false, 8, NUM_THREADS)          \
+    __CALL_IF(NUM_BITS, 3, N_BLOCKS, K_BLOCKS, false, 8, NUM_THREADS)          \
+    __CALL_IF(NUM_BITS, 4, N_BLOCKS, K_BLOCKS, false, 8, NUM_THREADS)        
+
+    template <typename scalar_t>
+    void marlin_mm_f16i4(const void* A, const void* B, void* C, void* s,
+        void* g_idx, void* perm, void* a_tmp, int* prob_m_ptr, int prob_m,
+        int prob_n, int prob_k, void* workspace, int num_bits,
+        bool has_act_order, bool is_k_full, int num_groups,
+        int group_size, int dev, cudaStream_t stream, int thread_k,
+        int thread_n, int sms, int max_par) {
+        TORCH_CHECK(num_bits == 4 || num_bits == 8,
+            "num_bits must be 4 or 8. Got = ", num_bits);
+        TORCH_CHECK(prob_m > 0 && prob_n > 0 && prob_k > 0, "Invalid MNK = [",
+            prob_m, ", ", prob_n, ", ", prob_k, "]");
+
+        int tot_m = prob_m;
+        int tot_m_blocks = div_ceil(tot_m, 16);
+        int pad = 16 * tot_m_blocks - tot_m;
+
+        if (sms == -1) {
+            cudaDeviceGetAttribute(&sms, cudaDevAttrMultiProcessorCount, dev);
+        }
+
+        int max_shared_mem = 0;
+        cudaDeviceGetAttribute(&max_shared_mem,
+            cudaDevAttrMaxSharedMemoryPerBlockOptin, dev);
+        TORCH_CHECK(max_shared_mem > 0);
+
+        // Set thread config
+        exec_config_t exec_cfg;
+        if (thread_k != -1 && thread_n != -1) {
+            // User-defined config
+            exec_cfg = exec_config_t{
+                4, thread_config_t{thread_k, thread_n, default_threads} };
+        }
+        else {
+            // Auto config
+            exec_cfg = determine_thread_config(prob_m, prob_n, prob_k, num_bits,
+                group_size, has_act_order, is_k_full,
+                max_shared_mem);
+        }
+
+        TORCH_CHECK(
+            exec_cfg.max_m_blocks > 0 &&
+            is_valid_config(exec_cfg.tb_cfg, exec_cfg.max_m_blocks, prob_m,
+                prob_n, prob_k, num_bits, group_size, has_act_order,
+                is_k_full, max_shared_mem),
+            "Invalid thread config: max_m_blocks = ", exec_cfg.max_m_blocks,
+            ", thread_k = ", exec_cfg.tb_cfg.thread_k,
+            ", thread_n = ", exec_cfg.tb_cfg.thread_n,
+            ", num_threads = ", exec_cfg.tb_cfg.num_threads, " for MKN = [", prob_m,
+            ", ", prob_k, ", ", prob_n, "] and num_bits = ", num_bits,
+            ", group_size = ", group_size, ", has_act_order = ", has_act_order,
+            ", is_k_full = ", is_k_full, ", max_shared_mem = ", max_shared_mem);
+
+        int num_threads = exec_cfg.tb_cfg.num_threads;
+        thread_k = exec_cfg.tb_cfg.thread_k;
+        thread_n = exec_cfg.tb_cfg.thread_n;
+
+        int thread_k_blocks = thread_k / 16;
+        int thread_n_blocks = thread_n / 16;
+
+        int blocks = sms;
+
+        TORCH_CHECK(prob_n % thread_n == 0, "prob_n = ", prob_n,
+            " is not divisible by thread_n = ", thread_n);
+        TORCH_CHECK(prob_k % thread_k == 0, "prob_k = ", prob_k,
+            " is not divisible by thread_k = ", thread_k);
+
+        int group_blocks = 0;
+        if (has_act_order) {
+            if (is_k_full) {
+                TORCH_CHECK(group_size != -1);
+                group_blocks = group_size / 16;
+                TORCH_CHECK(prob_k % group_blocks == 0, "prob_k = ", prob_k,
+                    " is not divisible by group_blocks = ", group_blocks);
+            }
+            else {
+                TORCH_CHECK(group_size == 0);
+                group_blocks = 0;
+            }
+
+        }
+        else {
+            if (group_size == -1) {
+                group_blocks = -1;
+            }
+            else {
+                group_blocks = group_size / 16;
+                TORCH_CHECK(prob_k % group_blocks == 0, "prob_k = ", prob_k,
+                    " is not divisible by group_blocks = ", group_blocks);
+            }
+        }
+
+        const int4* A_ptr = (const int4*)A;
+        const int4* B_ptr = (const int4*)B;
+        int4* C_ptr = (int4*)C;
+        const int4* s_ptr = (const int4*)s;
+        const int* g_idx_ptr = (const int*)g_idx;
+        const int* perm_ptr = (const int*)perm;
+        int4* a_tmp_ptr = (int4*)a_tmp;
+
+        int* locks = (int*)workspace;
+
+        if (has_act_order) {
+            // Permute A columns
+            int block_rows = div_ceil(prob_m, blocks);
+            permute_cols_kernel << <blocks, default_threads, 0, stream >> > (
+                A_ptr, perm_ptr, a_tmp_ptr, prob_m, prob_k, block_rows);
+            A_ptr = a_tmp_ptr;
+        }
+
+        // If we have a full K, then we can run the non-act-order version of Marlin
+        // (since the weight rows are reordered by increasing group ids, and by
+        // having a full K, we have full original groups)
+        if (is_k_full) {
+            has_act_order = false;
+        }
+
+        // Main loop
+        for (int i = 0; i < tot_m_blocks; i += exec_cfg.max_m_blocks) {
+            int thread_m_blocks = tot_m_blocks - i;
+            prob_m = tot_m - 16 * i;
+            int par = 1;
+            if (thread_m_blocks > exec_cfg.max_m_blocks) {
+                // Note that parallel > 1 currently only works for inputs without
+                // any padding
+                par = (16 * thread_m_blocks - pad) / (16 * exec_cfg.max_m_blocks);
+                if (par > max_par)
+                    par = max_par;
+                prob_m = (16 * exec_cfg.max_m_blocks) * par;
+                i += exec_cfg.max_m_blocks * (par - 1);
+                thread_m_blocks = exec_cfg.max_m_blocks;
+            }
+
+            // Define kernel configurations
+#define undefined_error                                                        \
+    TORCH_CHECK(false, "Unsupported shapes: MNK = [" + str(prob_m) + ", " +    \
+                           str(prob_n) + ", " + str(prob_k) + "]" +            \
+                           ", has_act_order = " + str(has_act_order) +         \
+                           ", num_groups = " + str(num_groups) +               \
+                           ", group_size = " + str(group_size) +               \
+                           ", thread_m_blocks = " + str(thread_m_blocks) +     \
+                           ", thread_n_blocks = " + str(thread_n_blocks) +     \
+                           ", thread_k_blocks = " + str(thread_k_blocks));
+
+        /* std::cout << "MNK = [" + str(prob_m) + ", " + \
+             str(prob_n) + ", " + str(prob_k) + "]" + \
+             ", has_act_order = " + str(has_act_order) + \
+             ", num_groups = " + str(num_groups) + \
+             ", group_size = " + str(group_size) + \
+             ", thread_m_blocks = " + str(thread_m_blocks) + \
+             ", thread_n_blocks = " + str(thread_n_blocks) + \
+             ", thread_k_blocks = " + str(thread_k_blocks) << std::endl;*/
+
+             /*if (false) {
+             }
+             // CALL_IF(4, 32, 2, 256)
+             // CALL_IF(4, 16, 4, 256)
+             __CALL_IF(4, 1, 16, 4, false, 4, 256)
+             __CALL_IF(4, 2, 16, 4, false, 4, 256)
+             // CALL_IF(4, 8, 8, 256)
+             __CALL_IF(4, 1, 8, 8, false, 4, 256)
+             __CALL_IF(4, 2, 8, 8, false, 4, 256)
+             // CALL_IF(4, 16, 4, 128)
+             __CALL_IF(4, 1, 16, 4, false, 4, 128)
+             __CALL_IF(4, 2, 16, 4, false, 4, 128)
+             // CALL_IF(4, 8, 8, 128)
+             __CALL_IF(4, 1, 8, 8, false, 4, 128)
+             __CALL_IF(4, 2, 8, 8, false, 4, 128)
+             else {undefined_error}*/
+
+            if (num_bits == 4 && num_threads == 256)
+            {
+                if (false) {
+                }
+                CALL_IF(4, 32, 2, 256)
+                    CALL_IF(4, 16, 4, 256)
+                    CALL_IF(4, 8, 8, 256)
+                else {
+                    undefined_error
+                }
+            }
+            else if (num_bits == 4 && num_threads == 128)
+            {
+                if (false) {
+                }
+                CALL_IF(4, 8, 4, 128)
+                    CALL_IF(4, 16, 4, 128)
+                    CALL_IF(4, 4, 8, 128)
+                else {
+                    undefined_error
+                }
+            }
+            // else if (num_bits == 8 && num_threads == 256)
+            // {
+            //     if (false) {
+            //     }
+            //     CALL_IF(8, 32, 2, 256)
+            //     CALL_IF(8, 16, 4, 256)
+            //     CALL_IF(8, 8, 8, 256)
+            //     else {
+            //         undefined_error
+            //     }
+            // }
+            // else if (num_bits == 8 && num_threads == 128)
+            // {
+            //     if (false) {
+            //     }
+            //     CALL_IF(8, 8, 4, 128)
+            //     CALL_IF(8, 16, 4, 128)
+            //     CALL_IF(8, 4, 8, 128)
+            //     else {
+            //         undefined_error
+            //     }
+            // }
+            else {
+                undefined_error
+            }
+
+            A_ptr += 16 * thread_m_blocks * (prob_k / 8) * par;
+            C_ptr += 16 * thread_m_blocks * (prob_n / 8) * par;
+        }
+    }
+
+} // namespace gptq_marlin
+
+torch::Tensor gptq_marlin_gemm(torch::Tensor& a, torch::Tensor& b_q_weight,
+    torch::Tensor& b_scales, torch::Tensor& g_idx,
+    torch::Tensor& perm, torch::Tensor& workspace,
+    int64_t num_bits, torch::Tensor size_m_tensor, int64_t size_m, int64_t size_n,
+    int64_t size_k, int sms, bool is_k_full) {
+    const at::cuda::OptionalCUDAGuard device_guard(device_of(a));
+    // Verify num_bits
+    TORCH_CHECK(num_bits == 4 || num_bits == 8,
+        "num_bits must be 4 or 8. Got = ", num_bits);
+    int pack_factor = 32 / num_bits;
+
+    // Verify A
+    TORCH_CHECK(a.size(0) == size_m, "Shape mismatch: a.size(0) = ", a.size(0),
+        ", size_m = ", size_m);
+    TORCH_CHECK(a.size(1) == size_k, "Shape mismatch: a.size(1) = ", a.size(1),
+        ", size_k = ", size_k);
+
+    // Verify B
+    TORCH_CHECK(size_k % gptq_marlin::tile_size == 0, "size_k = ", size_k,
+        " is not divisible by tile_size = ", gptq_marlin::tile_size);
+    TORCH_CHECK((size_k / gptq_marlin::tile_size) == b_q_weight.size(0),
+        "Shape mismatch: b_q_weight.size(0) = ", b_q_weight.size(0),
+        ", size_k = ", size_k,
+        ", tile_size = ", gptq_marlin::tile_size);
+    TORCH_CHECK(b_q_weight.size(1) % gptq_marlin::tile_size == 0,
+        "b_q_weight.size(1) = ", b_q_weight.size(1),
+        " is not divisible by tile_size = ", gptq_marlin::tile_size);
+    int actual_size_n =
+        (b_q_weight.size(1) / gptq_marlin::tile_size) * pack_factor;
+    TORCH_CHECK(size_n == actual_size_n, "size_n = ", size_n,
+        ", actual_size_n = ", actual_size_n);
+
+    // Verify device and strides
+    TORCH_CHECK(a.device().is_cuda(), "A is not on GPU");
+    TORCH_CHECK(a.is_contiguous(), "A is not contiguous");
+
+    TORCH_CHECK(b_q_weight.device().is_cuda(), "b_q_weight is not on GPU");
+    TORCH_CHECK(b_q_weight.is_contiguous(), "b_q_weight is not contiguous");
+
+    TORCH_CHECK(b_scales.device().is_cuda(), "b_scales is not on GPU");
+    TORCH_CHECK(b_scales.is_contiguous(), "b_scales is not contiguous");
+
+    TORCH_CHECK(g_idx.device().is_cuda(), "g_idx is not on GPU");
+    TORCH_CHECK(g_idx.is_contiguous(), "g_idx is not contiguous");
+
+    TORCH_CHECK(perm.device().is_cuda(), "perm is not on GPU");
+    TORCH_CHECK(perm.is_contiguous(), "perm is not contiguous");
+
+    // Alloc buffers
+    auto options = torch::TensorOptions().dtype(a.dtype()).device(a.device());
+    torch::Tensor c = torch::empty({ size_m, size_n }, options);
+    torch::Tensor a_tmp = torch::empty({ size_m, size_k }, options);
+
+    // thread_k: `k` size of a thread_tile in `weights` (can usually be left as
+    // auto -1)
+    int thread_k = -1;
+    // thread_n: `n` size of a thread_tile in `weights` (can usually be left as
+    // auto -1)
+    int thread_n = -1;
+    // sms: number of SMs to use for the kernel (can usually be left as auto -1)
+    // int sms = -1; //zbx
+
+    // Verify g_idx and perm
+    TORCH_CHECK((g_idx.size(0) == 0 && perm.size(0) == 0) ||
+        (g_idx.size(0) == size_k && perm.size(0) == size_k),
+        "Unexpected g_idx.size(0) = ", g_idx.size(0),
+        " and perm.size(0) = ", perm.size(0),
+        ", where size_k = ", size_k);
+
+    // Detect groupsize and act_order
+    int num_groups = -1;
+    int group_size = -1;
+    bool has_act_order = g_idx.size(0) != 0;
+
+    int b_rank = b_scales.sizes().size();
+    TORCH_CHECK(b_rank == 2, "b_scales rank = ", b_rank, " is not 2");
+    TORCH_CHECK(b_scales.size(1) == size_n,
+        "b_scales dim 1 = ", b_scales.size(1),
+        " is not size_n = ", size_n);
+    num_groups = b_scales.size(0);
+
+    if (has_act_order) {
+        if (is_k_full) {
+            TORCH_CHECK(num_groups > 1,
+                "For act_order, num_groups must be > 1");
+            TORCH_CHECK(size_k % num_groups == 0, "size_k = ", size_k,
+                ", is not divisible by num_groups = ", num_groups);
+            group_size = size_k / num_groups;
+        }
+        else {
+            group_size = 0;
+        }
+
+    }
+    else {
+        if (num_groups > 1) {
+            TORCH_CHECK(
+                size_k % num_groups == 0, "size_k = ", size_k,
+                ", is not divisible by b_scales.size(0) = ", b_scales.size(0));
+            group_size = size_k / num_groups;
+        }
+        else {
+            group_size = -1;
+        }
+    }
+
+    // Verify workspace size
+    TORCH_CHECK(
+        size_n % gptq_marlin::min_thread_n == 0, "size_n = ", size_n,
+        ", is not divisible by min_thread_n = ", gptq_marlin::min_thread_n);
+    int min_workspace_size =
+        (size_n / gptq_marlin::min_thread_n) * gptq_marlin::max_par;
+    TORCH_CHECK(workspace.numel() >= min_workspace_size,
+        "workspace.numel = ", workspace.numel(),
+        " is below min_workspace_size = ", min_workspace_size);
+
+    int dev = a.get_device();
+    if (a.scalar_type() == at::ScalarType::Half) {
+        gptq_marlin::marlin_mm_f16i4<half>(
+            a.data_ptr<at::Half>(), b_q_weight.data_ptr(),
+            c.data_ptr<at::Half>(), b_scales.data_ptr<at::Half>(),
+            g_idx.data_ptr(), perm.data_ptr(), a_tmp.data_ptr<at::Half>(),
+            size_m_tensor.data_ptr<int>(),
+            size_m, size_n, size_k, workspace.data_ptr(), num_bits,
+            has_act_order, is_k_full, num_groups, group_size, dev,
+            at::cuda::getCurrentCUDAStream(dev), thread_k, thread_n, sms,
+            gptq_marlin::max_par);
+    }
+    else if (a.scalar_type() == at::ScalarType::BFloat16) {
+        gptq_marlin::marlin_mm_f16i4<nv_bfloat16>(
+            a.data_ptr<at::BFloat16>(), b_q_weight.data_ptr(),
+            c.data_ptr<at::BFloat16>(), b_scales.data_ptr<at::BFloat16>(),
+            g_idx.data_ptr(), perm.data_ptr(), a_tmp.data_ptr<at::BFloat16>(),
+            size_m_tensor.data_ptr<int>(),
+            size_m, size_n, size_k, workspace.data_ptr(), num_bits,
+            has_act_order, is_k_full, num_groups, group_size, dev,
+            at::cuda::getCurrentCUDAStream(dev), thread_k, thread_n, sms,
+            gptq_marlin::max_par);
+    }
+    else {
+        TORCH_CHECK(false,
+            "gpt_marlin_gemm only supports bfloat16 and float16");
+    }
+
+    return c;
+}
+
+#endif
\ No newline at end of file

csrc/custom_marlin/gptq_marlin/gptq_marlin.cuh

+// Adapted from
+// https://github.com/vllm-project/vllm/tree/main/csrc/quantization/gptq_marlin
+// Copyrigth 2024 The vLLM team.
+// Copyright (c) 2024 by KVCache.AI, All Rights Reserved.
+#pragma once
+
+#include <torch/all.h>
+
+#include <ATen/cuda/CUDAContext.h>
+#include <c10/cuda/CUDAGuard.h>
+#include <cuda.h>
+#include <cuda_fp16.h>
+#include <cuda_runtime.h>
+#include <iostream>
+
+namespace gptq_marlin {
+
+// 8 warps are a good choice since every SM has 4 schedulers and having more
+// than 1 warp per schedule allows some more latency hiding. At the same time,
+// we want relatively few warps to have many registers per warp and small tiles.
+static constexpr int default_threads = 256;
+
+static constexpr int pipe_stages =
+    4; // 4 pipeline stages fit into shared memory
+
+static constexpr int min_thread_n = 64;
+static constexpr int min_thread_k = 64;
+
+static constexpr int tile_size = 16;
+static constexpr int max_par = 16;
+
+template <typename T, int n> struct Vec {
+    T elems[n];
+    __device__ T &operator[](int i) { return elems[i]; }
+};
+
+using I4 = Vec<int, 4>;
+
+constexpr int div_ceil(int a, int b) { return (a + b - 1) / b; }
+
+#if defined(__CUDA_ARCH__) && __CUDA_ARCH__ < 800
+// No support for async
+#else
+
+__device__ inline void cp_async4_pred(void *smem_ptr, const void *glob_ptr,
+                                      bool pred = true) {
+    const int BYTES = 16;
+    uint32_t smem = static_cast<uint32_t>(__cvta_generic_to_shared(smem_ptr));
+    asm volatile("{\n"
+                 "   .reg .pred p;\n"
+                 "   setp.ne.b32 p, %0, 0;\n"
+                 "   @p cp.async.cg.shared.global [%1], [%2], %3;\n"
+                 "}\n" ::"r"((int)pred),
+                 "r"(smem), "l"(glob_ptr), "n"(BYTES));
+}
+
+__device__ inline void cp_async4(void *smem_ptr, const void *glob_ptr) {
+    const int BYTES = 16;
+    uint32_t smem = static_cast<uint32_t>(__cvta_generic_to_shared(smem_ptr));
+    asm volatile("{\n"
+                 "   cp.async.cg.shared.global [%0], [%1], %2;\n"
+                 "}\n" ::"r"(smem),
+                 "l"(glob_ptr), "n"(BYTES));
+}
+
+__device__ inline void cp_async_fence() {
+    asm volatile("cp.async.commit_group;\n" ::);
+}
+
+template <int n> __device__ inline void cp_async_wait() {
+    asm volatile("cp.async.wait_group %0;\n" ::"n"(n));
+}
+
+#endif
+
+} // namespace gptq_marlin
\ No newline at end of file

csrc/custom_marlin/gptq_marlin/gptq_marlin_dtypes.cuh

+// Adapted from
+// https://github.com/vllm-project/vllm/tree/main/csrc/quantization/gptq_marlin
+// Copyrigth 2024 The vLLM team.
+// Copyright (c) 2024 by KVCache.AI, All Rights Reserved.
+#ifndef _data_types_cuh
+#define _data_types_cuh
+#include "gptq_marlin.cuh"
+#include <cuda_bf16.h>
+#include <cuda_fp16.h>
+
+namespace gptq_marlin {
+
+template <typename scalar_t> class ScalarType {};
+
+template <> class ScalarType<half> {
+  public:
+    using scalar_t = half;
+    using scalar_t2 = half2;
+
+    // Matrix fragments for tensor core instructions; their precise layout is
+    // documented here:
+    // https://docs.nvidia.com/cuda/parallel-thread-execution/index.html#matrix-fragments-for-mma-m16n8k16-with-floating-point-type
+    using FragA = Vec<half2, 4>;
+    using FragB = Vec<half2, 2>;
+    using FragC = Vec<float, 4>;
+    using FragS = Vec<half2, 1>;
+
+    static __device__ float inline num2float(const half x) {
+        return __half2float(x);
+    }
+
+    static __device__ half2 inline num2num2(const half x) {
+        return __half2half2(x);
+    }
+
+    static __device__ half2 inline nums2num2(const half x1, const half x2) {
+        return __halves2half2(x1, x2);
+    }
+
+    static __host__ __device__ half inline float2num(const float x) {
+        return __float2half(x);
+    }
+};
+
+template <> class ScalarType<nv_bfloat16> {
+  public:
+    using scalar_t = nv_bfloat16;
+    using scalar_t2 = nv_bfloat162;
+
+    using FragA = Vec<nv_bfloat162, 4>;
+    using FragB = Vec<nv_bfloat162, 2>;
+    using FragC = Vec<float, 4>;
+    using FragS = Vec<nv_bfloat162, 1>;
+
+#if defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 800
+    static __device__ float inline num2float(const nv_bfloat16 x) {
+        return __bfloat162float(x);
+    }
+
+    static __device__ nv_bfloat162 inline num2num2(const nv_bfloat16 x) {
+        return __bfloat162bfloat162(x);
+    }
+
+    static __device__ nv_bfloat162 inline nums2num2(const nv_bfloat16 x1,
+                                                    const nv_bfloat16 x2) {
+        return __halves2bfloat162(x1, x2);
+    }
+
+    static __host__ __device__ nv_bfloat16 inline float2num(const float x) {
+        return __float2bfloat16(x);
+    }
+#endif
+};
+
+} // namespace gptq_marlin
+
+#endif
\ No newline at end of file

csrc/custom_marlin/gptq_marlin/gptq_marlin_repack.cu

+#include "gptq_marlin.cuh"
+
+namespace gptq_marlin {
+
+static constexpr int repack_stages = 8;
+
+static constexpr int repack_threads = 256;
+
+static constexpr int tile_k_size = tile_size;
+static constexpr int tile_n_size = tile_k_size * 4;
+
+#if defined(__CUDA_ARCH__) && __CUDA_ARCH__ < 800
+
+template <int const num_threads, int const num_bits, bool const has_perm>
+__global__ void marlin_repack_kernel(
+    uint32_t const* __restrict__ b_q_weight_ptr,
+    uint32_t const* __restrict__ perm_ptr, uint32_t* __restrict__ out_ptr,
+    int size_k, int size_n) {}
+
+}  // namespace gptq_marlin
+
+torch::Tensor gptq_marlin_repack(torch::Tensor& b_q_weight, torch::Tensor& perm,
+                                 int64_t size_k, int64_t size_n,
+                                 int64_t num_bits) {
+  TORCH_CHECK_NOT_IMPLEMENTED(
+      false, "marlin_repack_from_gptq(..) requires CUDA_ARCH >= 8.0");
+  return torch::empty({1, 1});
+}
+
+#else
+
+template <int const num_threads, int const num_bits, bool const has_perm>
+__global__ void marlin_repack_kernel(
+    uint32_t const* __restrict__ b_q_weight_ptr,
+    uint32_t const* __restrict__ perm_ptr, uint32_t* __restrict__ out_ptr,
+    int size_k, int size_n) {
+  constexpr int pack_factor = 32 / num_bits;
+
+  int k_tiles = size_k / tile_k_size;
+  int n_tiles = size_n / tile_n_size;
+  int block_k_tiles = div_ceil(k_tiles, gridDim.x);
+
+  int start_k_tile = blockIdx.x * block_k_tiles;
+  if (start_k_tile >= k_tiles) {
+    return;
+  }
+
+  int finish_k_tile = min(start_k_tile + block_k_tiles, k_tiles);
+
+  // Wait until the next thread tile has been loaded to shared memory.
+  auto wait_for_stage = [&]() {
+    // We only have `stages - 2` active fetches since we are double buffering
+    // and can only issue the next fetch when it is guaranteed that the previous
+    // shared memory load is fully complete (as it may otherwise be
+    // overwritten).
+    cp_async_wait<repack_stages - 2>();
+    __syncthreads();
+  };
+
+  extern __shared__ int4 sh[];
+
+  constexpr int perm_size = tile_k_size / 4;
+
+  int4* sh_perm_ptr = sh;
+  int4* sh_pipe_ptr = sh_perm_ptr;
+  if constexpr (has_perm) {
+    sh_pipe_ptr += perm_size;
+  }
+
+  constexpr int tile_ints = tile_k_size / pack_factor;
+
+  constexpr int stage_n_threads = tile_n_size / 4;
+  constexpr int stage_k_threads = has_perm ? tile_k_size : tile_ints;
+  constexpr int stage_size = stage_k_threads * stage_n_threads;
+
+  auto load_perm_to_shared = [&](int k_tile_id) {
+    int first_k_int4 = (k_tile_id * tile_k_size) / 4;
+
+    int4 const* perm_int4_ptr = reinterpret_cast<int4 const*>(perm_ptr);
+
+    if (threadIdx.x < perm_size) {
+      sh_perm_ptr[threadIdx.x] = perm_int4_ptr[first_k_int4 + threadIdx.x];
+    }
+    __syncthreads();
+  };
+
+  auto fetch_to_shared = [&](int pipe, int k_tile_id, int n_tile_id) {
+    if (n_tile_id >= n_tiles) {
+      cp_async_fence();
+      return;
+    }
+
+    int first_n = n_tile_id * tile_n_size;
+
+    int4* sh_ptr = sh_pipe_ptr + stage_size * pipe;
+
+    if constexpr (has_perm) {
+      if (threadIdx.x < stage_size) {
+        int k_id = threadIdx.x / stage_n_threads;
+        int n_id = threadIdx.x % stage_n_threads;
+
+        uint32_t const* sh_perm_int_ptr =
+            reinterpret_cast<uint32_t const*>(sh_perm_ptr);
+
+        int src_k = sh_perm_int_ptr[k_id];
+        int src_k_packed = src_k / pack_factor;
+
+        cp_async4(
+            &sh_ptr[k_id * stage_n_threads + n_id],
+            reinterpret_cast<int4 const*>(&(
+                b_q_weight_ptr[src_k_packed * size_n + first_n + (n_id * 4)])));
+      }
+
+    } else {
+      if (threadIdx.x < stage_size) {
+        int k_id = threadIdx.x / stage_n_threads;
+        int n_id = threadIdx.x % stage_n_threads;
+
+        int first_k = k_tile_id * tile_k_size;
+        int first_k_packed = first_k / pack_factor;
+
+        cp_async4(&sh_ptr[k_id * stage_n_threads + n_id],
+                  reinterpret_cast<int4 const*>(
+                      &(b_q_weight_ptr[(first_k_packed + k_id) * size_n +
+                                       first_n + (n_id * 4)])));
+      }
+    }
+
+    cp_async_fence();
+  };
+
+  auto repack_tile = [&](int pipe, int k_tile_id, int n_tile_id) {
+    if (n_tile_id >= n_tiles) {
+      return;
+    }
+
+    int warp_id = threadIdx.x / 32;
+    int th_id = threadIdx.x % 32;
+
+    if (warp_id >= 4) {
+      return;
+    }
+
+    int tc_col = th_id / 4;
+    int tc_row = (th_id % 4) * 2;
+
+    constexpr int tc_offsets[4] = {0, 1, 8, 9};
+
+    int cur_n = warp_id * 16 + tc_col;
+
+    constexpr int sh_stride = 64;
+    constexpr uint32_t mask = (1 << num_bits) - 1;
+
+    int4* sh_stage_ptr = sh_pipe_ptr + stage_size * pipe;
+    uint32_t* sh_stage_int_ptr = reinterpret_cast<uint32_t*>(sh_stage_ptr);
+
+    uint32_t* sh_perm_int_ptr = reinterpret_cast<uint32_t*>(sh_perm_ptr);
+
+    uint32_t vals[8];
+
+    if constexpr (has_perm) {
+      for (int i = 0; i < 4; i++) {
+        int k_idx = tc_row + tc_offsets[i];
+
+        uint32_t src_k = sh_perm_int_ptr[k_idx];
+        uint32_t src_k_pos = src_k % pack_factor;
+
+        uint32_t b1_val = sh_stage_int_ptr[k_idx * sh_stride + cur_n];
+        uint32_t b1_cur_val = (b1_val >> (src_k_pos * num_bits)) & mask;
+
+        uint32_t b2_val = sh_stage_int_ptr[k_idx * sh_stride + cur_n + 8];
+        uint32_t b2_cur_val = (b2_val >> (src_k_pos * num_bits)) & mask;
+
+        vals[i] = b1_cur_val;
+        vals[4 + i] = b2_cur_val;
+      }
+
+    } else {
+      uint32_t b1_vals[tile_ints];
+      uint32_t b2_vals[tile_ints];
+
+  #pragma unroll
+      for (int i = 0; i < tile_ints; i++) {
+        b1_vals[i] = sh_stage_int_ptr[cur_n + sh_stride * i];
+        b2_vals[i] = sh_stage_int_ptr[cur_n + 8 + sh_stride * i];
+      }
+
+  #pragma unroll
+      for (int i = 0; i < 4; i++) {
+        int cur_elem = tc_row + tc_offsets[i];
+        int cur_int = cur_elem / pack_factor;
+        int cur_pos = cur_elem % pack_factor;
+
+        vals[i] = (b1_vals[cur_int] >> (cur_pos * num_bits)) & mask;
+        vals[4 + i] = (b2_vals[cur_int] >> (cur_pos * num_bits)) & mask;
+      }
+    }
+
+    constexpr int tile_size = tile_k_size * tile_n_size / pack_factor;
+    int out_offset = (k_tile_id * n_tiles + n_tile_id) * tile_size;
+
+    // Result of:
+    // https://github.com/NVIDIA/FasterTransformer/blob/main/src/fastertransformer/cutlass_extensions/include/cutlass_extensions/interleaved_numeric_conversion.h
+    if constexpr (num_bits == 4) {
+      constexpr int pack_idx[8] = {0, 2, 4, 6, 1, 3, 5, 7};
+
+      uint32_t res = 0;
+  #pragma unroll
+      for (int i = 0; i < 8; i++) {
+        res |= vals[pack_idx[i]] << (i * 4);
+      }
+
+      out_ptr[out_offset + th_id * 4 + warp_id] = res;
+
+    } else {
+      constexpr int pack_idx[4] = {0, 2, 1, 3};
+
+      uint32_t res1 = 0;
+      uint32_t res2 = 0;
+  #pragma unroll
+      for (int i = 0; i < 4; i++) {
+        res1 |= vals[pack_idx[i]] << (i * 8);
+        res2 |= vals[4 + pack_idx[i]] << (i * 8);
+      }
+
+      out_ptr[out_offset + th_id * 8 + (warp_id * 2) + 0] = res1;
+      out_ptr[out_offset + th_id * 8 + (warp_id * 2) + 1] = res2;
+    }
+  };
+
+  auto start_pipes = [&](int k_tile_id, int n_tile_id) {
+  #pragma unroll
+    for (int pipe = 0; pipe < repack_stages - 1; pipe++) {
+      fetch_to_shared(pipe, k_tile_id, n_tile_id + pipe);
+    }
+
+    wait_for_stage();
+  };
+  #pragma unroll
+  for (int k_tile_id = start_k_tile; k_tile_id < finish_k_tile; k_tile_id++) {
+    int n_tile_id = 0;
+
+    if constexpr (has_perm) {
+      load_perm_to_shared(k_tile_id);
+    }
+
+    start_pipes(k_tile_id, n_tile_id);
+
+    while (n_tile_id < n_tiles) {
+  #pragma unroll
+      for (int pipe = 0; pipe < repack_stages; pipe++) {
+        fetch_to_shared((pipe + repack_stages - 1) % repack_stages, k_tile_id,
+                        n_tile_id + pipe + repack_stages - 1);
+        repack_tile(pipe, k_tile_id, n_tile_id + pipe);
+        wait_for_stage();
+      }
+      n_tile_id += repack_stages;
+    }
+  }
+}
+
+}  // namespace gptq_marlin
+
+  #define CALL_IF(NUM_BITS, HAS_PERM)                                          \
+    else if (num_bits == NUM_BITS && has_perm == HAS_PERM) {                   \
+      cudaFuncSetAttribute(                                                    \
+          gptq_marlin::marlin_repack_kernel<gptq_marlin::repack_threads,       \
+                                            NUM_BITS, HAS_PERM>,               \
+          cudaFuncAttributeMaxDynamicSharedMemorySize, max_shared_mem);        \
+      gptq_marlin::marlin_repack_kernel<gptq_marlin::repack_threads, NUM_BITS, \
+                                        HAS_PERM>                              \
+          <<<blocks, gptq_marlin::repack_threads, max_shared_mem, stream>>>(   \
+              b_q_weight_ptr, perm_ptr, out_ptr, size_k, size_n);              \
+    }
+
+torch::Tensor gptq_marlin_repack(torch::Tensor& b_q_weight, torch::Tensor& perm,
+                                 int64_t size_k, int64_t size_n,
+                                 int64_t num_bits) {
+  // Verify compatibility with marlin tile of 16x64
+  TORCH_CHECK(size_k % gptq_marlin::tile_k_size == 0, "size_k = ", size_k,
+              " is not divisible by tile_k_size = ", gptq_marlin::tile_k_size);
+  TORCH_CHECK(size_n % gptq_marlin::tile_n_size == 0, "size_n = ", size_n,
+              " is not divisible by tile_n_size = ", gptq_marlin::tile_n_size);
+
+  TORCH_CHECK(num_bits == 4 || num_bits == 8,
+              "num_bits must be 4 or 8. Got = ", num_bits);
+  int const pack_factor = 32 / num_bits;
+
+  // Verify B
+  TORCH_CHECK((size_k / pack_factor) == b_q_weight.size(0),
+              "Shape mismatch: b_q_weight.size(0) = ", b_q_weight.size(0),
+              ", size_k = ", size_k, ", pack_factor = ", pack_factor);
+  TORCH_CHECK(b_q_weight.size(1) == size_n,
+              "b_q_weight.size(1) = ", b_q_weight.size(1),
+              " is not size_n = ", size_n);
+
+  // Verify device and strides
+  TORCH_CHECK(b_q_weight.device().is_cuda(), "b_q_weight is not on GPU");
+  TORCH_CHECK(b_q_weight.is_contiguous(), "b_q_weight is not contiguous");
+  TORCH_CHECK(b_q_weight.dtype() == at::kInt, "b_q_weight type is not kInt");
+
+  TORCH_CHECK(perm.device().is_cuda(), "perm is not on GPU");
+  TORCH_CHECK(perm.is_contiguous(), "perm is not contiguous");
+  TORCH_CHECK(perm.dtype() == at::kInt, "perm type is not at::kInt");
+
+  // Alloc buffers
+  const at::cuda::OptionalCUDAGuard device_guard(device_of(b_q_weight));
+  auto options = torch::TensorOptions()
+                     .dtype(b_q_weight.dtype())
+                     .device(b_q_weight.device());
+  torch::Tensor out =
+      torch::empty({size_k / gptq_marlin::tile_size,
+                    size_n * gptq_marlin::tile_size / pack_factor},
+                   options);
+
+  // Detect if there is act_order
+  bool has_perm = perm.size(0) != 0;
+
+  // Get ptrs
+  uint32_t const* b_q_weight_ptr =
+      reinterpret_cast<uint32_t const*>(b_q_weight.data_ptr());
+  uint32_t const* perm_ptr = reinterpret_cast<uint32_t const*>(perm.data_ptr());
+  uint32_t* out_ptr = reinterpret_cast<uint32_t*>(out.data_ptr());
+
+  // Get dev info
+  int dev = b_q_weight.get_device();
+  cudaStream_t stream = at::cuda::getCurrentCUDAStream(dev);
+  int blocks;
+  cudaDeviceGetAttribute(&blocks, cudaDevAttrMultiProcessorCount, dev);
+
+  int max_shared_mem = 0;
+  cudaDeviceGetAttribute(&max_shared_mem,
+                         cudaDevAttrMaxSharedMemoryPerBlockOptin, dev);
+  TORCH_CHECK(max_shared_mem > 0);
+
+  if (false) {
+  }
+  CALL_IF(4, false)
+  CALL_IF(4, true)
+  CALL_IF(8, false)
+  CALL_IF(8, true)
+  else {
+    TORCH_CHECK(false, "Unsupported repack config: num_bits = ", num_bits,
+                ", has_perm = ", has_perm);
+  }
+
+  return out;
+}
+
+#endif
\ No newline at end of file

csrc/custom_marlin/gptq_marlin/ops.h

+/**
+ * @Description  :
+ * @Author       : Azure
+ * @Date         : 2024-07-22 09:27:55
+ * @Version      : 1.0.0
+ * @LastEditors  : Azure
+ * @LastEditTime : 2024-07-26 08:35:00
+ * @Copyright (c) 2024 by KVCache.AI, All Rights Reserved.
+ **/
+#pragma once
+
+#include <torch/extension.h>
+#include <torch/library.h>
+#include <torch/torch.h>
+
+torch::Tensor gptq_marlin_gemm(torch::Tensor &a, torch::Tensor &b_q_weight,
+                               torch::Tensor &b_scales, torch::Tensor &g_idx,
+                               torch::Tensor &perm, torch::Tensor &workspace,
+                               int64_t num_bits, torch::Tensor size_m_tensor, int64_t size_m, int64_t size_n,
+                               int64_t size_k, int sms, bool is_k_full);
+
+torch::Tensor gptq_marlin_repack(torch::Tensor& b_q_weight, torch::Tensor&perm,
+                                 int64_t size_k, int64_t size_n,
+                                 int64_t num_bits);
\ No newline at end of file

csrc/custom_marlin/setup.py

+from setuptools import setup, Extension
+from torch.utils import cpp_extension
+from torch.utils.cpp_extension import BuildExtension, CUDAExtension
+setup(
+    name='vLLMMarlin',
+    ext_modules=[
+        CUDAExtension(
+            'vLLMMarlin', [
+                #'custom_gguf/dequant.cu',
+                'binding.cpp',
+                'gptq_marlin/gptq_marlin.cu',
+                'gptq_marlin/gptq_marlin_repack.cu',
+            ],
+            extra_compile_args={
+                'cxx': ['-O3'],
+                'nvcc': [
+                    '-O3',
+                    '--use_fast_math',
+                    '-Xcompiler', '-fPIC',
+                ]
+            },
+        )
+    ],
+    cmdclass={'build_ext': BuildExtension}
+)
\ No newline at end of file

csrc/custom_marlin/test_cuda_graph.py

+import csv
+import torch
+import torch.nn as nn
+import vLLMMarlin
+torch.set_grad_enabled(False)
+from utils.marlin_utils import (
+	MarlinWorkspace,
+	marlin_quantize,
+	GPTQ_MARLIN_MIN_THREAD_N,
+	GPTQ_MARLIN_MIN_THREAD_K,
+	GPTQ_MARLIN_MAX_PARALLEL,
+)
+
+def setup_seed(seed):
+	torch.manual_seed(seed)
+	torch.cuda.manual_seed_all(seed)
+
+setup_seed(20241223)
+
+torch.set_grad_enabled(False)
+torch.set_default_dtype(torch.bfloat16)
+global_dtype=torch.bfloat16
+global_device=torch.device("cuda",0)
+global_num_cases:int=int(50)
+torch.cuda.set_device(0)
+torch.backends.cudnn.enabled =True
+torch.backends.cudnn.benchmark = True
+
+max_batch_size = 512
+max_tp = 8
+L2_size = 73728 * 1024
+
+def get_usable_mem():
+	properties = torch.cuda.get_device_properties(global_device)
+	#print(f"Total memory: {properties.total_memory / (1024 ** 3):.2f} GB")
+	allocated_memory = torch.cuda.memory_allocated(global_device)
+	#print(f"Currently allocated memory: {allocated_memory / (1024 ** 2):.2f} MB")
+	reserved_memory = torch.cuda.memory_reserved(global_device)
+	#print(f"Currently reserved memory: {reserved_memory / (1024 ** 2):.2f} MB")
+	return properties.total_memory - 512 * 1024 ** 2 - allocated_memory# - reserved_memory
+
+def exp_range(start, stop, step = 2):
+	now = start
+	while now <= stop:
+		yield now
+		now *= step
+
+def timing(func, iters, epochs=100):
+	#warmup
+	for idx in range(iters):
+		func(idx)
+		
+	torch.cuda.synchronize()
+	cuda_graph = torch.cuda.CUDAGraph()
+	with torch.cuda.graph(cuda_graph):
+		for idx in range(iters):
+			func(idx)
+
+	for _ in range(2000):
+		cuda_graph.replay()
+
+	start_event = torch.cuda.Event(enable_timing=True)
+	end_event = torch.cuda.Event(enable_timing=True)
+	stream = torch.cuda.Stream()
+	torch.cuda.synchronize()
+	#with torch.cuda.stream(stream):
+	start_event.record()
+	for _ in range(10):
+		cuda_graph.replay()
+	end_event.record()
+	torch.cuda.synchronize()
+	elapsed_time_ms0 = start_event.elapsed_time(end_event)
+	
+	start_event = torch.cuda.Event(enable_timing=True)
+	end_event = torch.cuda.Event(enable_timing=True)
+	torch.cuda.synchronize()
+	#with torch.cuda.stream(stream):
+	start_event.record()
+	for _ in range(epochs+10):
+		cuda_graph.replay()
+	end_event.record()
+	torch.cuda.synchronize()
+	elapsed_time_ms = start_event.elapsed_time(end_event) - elapsed_time_ms0
+	
+	#print(elapsed_time_ms0, elapsed_time_ms)
+	return elapsed_time_ms/iters/epochs
+
+class LinearMarlin(nn.Linear):
+	marlin_q_w: torch.Tensor
+	marlin_s: torch.Tensor
+	g_idx: torch.Tensor
+	sort_indices: torch.Tensor
+	has_bias: bool
+	def __init__(
+		self,
+		in_features,
+		out_features,
+		bias = False,
+		device: str = "cuda",
+		num_bits: int = 4,  # 4-bit/8-bit is supported
+		group_size: int = 64,  # -1, 32, 64, 128
+		act_order: bool = False,
+		is_k_full=True,
+		sms = -1, # sms in GPU
+		**kwargs,
+	):
+		self.padding = False
+		assert device.lower() != "cpu", "Marlin quantized linear only supports GPU device"
+		if in_features%GPTQ_MARLIN_MIN_THREAD_K!=0 or out_features%GPTQ_MARLIN_MIN_THREAD_K!=0:
+			#print(f"warning!, in_features={in_features} or out_features={out_features} is undivisible by GPTQ_MARLIN_MIN_THREAD_K={GPTQ_MARLIN_MIN_THREAD_K} and GPTQ_MARLIN_MIN_THREAD_N={GPTQ_MARLIN_MIN_THREAD_N}, padding")
+			self.padding = True
+			self.orin_in_features = in_features
+			self.orin_out_features = out_features
+			in_features = (in_features+GPTQ_MARLIN_MIN_THREAD_K-1)//GPTQ_MARLIN_MIN_THREAD_K*GPTQ_MARLIN_MIN_THREAD_K
+			out_features = (out_features+GPTQ_MARLIN_MIN_THREAD_N-1)//GPTQ_MARLIN_MIN_THREAD_N*GPTQ_MARLIN_MIN_THREAD_N
+			#print(f"After padding: in_features={in_features}, out_features={out_features}")
+			
+
+		super().__init__(in_features, out_features, bias, device)
+		self.has_bias = bias
+		self.device = device
+		self.num_bits = num_bits
+		self.group_size = group_size
+		self.act_order = act_order
+		# TODO: optimize every shape GEMM
+		
+		blocks_k, blocks_n = in_features//128, out_features//128
+
+		self.sms = sms
+
+		self.is_k_full = is_k_full
+		
+		self.weight.requires_grad = False
+		self.weight.t_()
+		# Pack Marlin linear
+		#w_ref, marlin_q_w, marlin_s, g_idx, sort_indices, _ = marlin_quantize(
+		#    self.weight, self.num_bits, self.group_size, self.act_order
+		#)
+		marlin_q_w = torch.randint(int(-1e9), int(1e9), (in_features//16, out_features*2), device=device, dtype=torch.int)
+		marlin_s = torch.randn((in_features//64, out_features), device=device)
+		self.workspace = MarlinWorkspace(
+			self.out_features, GPTQ_MARLIN_MIN_THREAD_N, GPTQ_MARLIN_MAX_PARALLEL, self.device
+		)
+		self.marlin_q_w = marlin_q_w
+		self.marlin_s = marlin_s
+		self.g_idx = torch.empty((0), dtype=torch.int32, device=self.device)
+		self.sort_indices = torch.empty((0), dtype=torch.int32, device=self.device)
+		self.k = self.weight.shape[0]
+		self.n = self.weight.shape[1]
+		self.weight = None
+		"""
+		print(in_features, out_features)
+		print(marlin_q_w.shape)
+		print(marlin_q_w.dtype)
+		print(marlin_s.shape)
+		print(marlin_s.dtype)
+		print(self.workspace.scratch.shape)
+		print(self.workspace.scratch.dtype)
+		print(self.g_idx.shape)
+		print(self.g_idx.dtype)
+		print(self.sort_indices.shape)
+		print(self.sort_indices.dtype)
+		#print(w_ref.shape)
+		#print(w_ref.dtype)
+		"""
+		#w_ref = None
+
+	def forward(self, x: torch.Tensor, bsz_tensor: torch.Tensor) -> torch.Tensor:
+		# Only support input x as BF16 and FP16
+		x = x.to(self.device)
+		orig_shape = list(x.shape)
+		orig_dtype = x.dtype
+		x = x.reshape(-1, x.shape[-1])
+		if self.padding:
+			padding_input=torch.empty(x.shape[0], self.in_features, device=x.device, dtype=x.dtype)
+			padding_input[:,:self.orin_in_features] = x
+			x = padding_input
+		marlin_s = self.marlin_s.to(x.dtype)
+		#print(self.sms * ((orig_shape[0]+63)//64))
+		
+		sms = self.sms
+
+		x = vLLMMarlin.gptq_marlin_gemm(
+			x,
+			self.marlin_q_w,
+			marlin_s,
+			self.g_idx,
+			self.sort_indices,
+			self.workspace.scratch,
+			self.num_bits,
+			bsz_tensor,
+			x.shape[0],
+			self.n,
+			x.shape[-1],
+			sms,
+			self.is_k_full,
+		)
+		# TODO: don't padding bias
+		if self.has_bias:
+			x = x + self.bias
+		if self.padding:
+			x = x[:,:self.orin_out_features]
+			orig_shape[-1] = self.orin_out_features
+		else:
+			orig_shape[-1] = self.out_features
+		return x.reshape(orig_shape).to(orig_dtype)
+
+def benchLinearMarlin(input_dim, output_dim):#, out_file
+	print("benchmarking MLP Marlin")
+	print("-----------------------------------------------------------")
+	headers = ["batch_size", "tp", "used_time", "bandwidth GB/s", "TFLOPS", "cases", "padding", "sms"]
+	print(" | ".join(headers) + "\n")
+	rows = []
+	for batch_size in exp_range(1, 64):
+		for tp in exp_range(1, max_tp):
+			torch.cuda.empty_cache()
+			if output_dim % tp != 0:
+				continue
+			cur_output_dim = output_dim // tp
+			modules = []
+			inputs = []
+			data_size = int(0.53125*input_dim*cur_output_dim)
+			input_size = int(2*batch_size*input_dim)
+			output_size = int(2*batch_size*cur_output_dim)
+			usable_mem = get_usable_mem() - 2 * input_dim * cur_output_dim
+			min_cases = max(global_num_cases, (2*L2_size) // (data_size+input_size))
+			cases = int(min(min_cases, (usable_mem * 0.8) // (data_size+input_size)))
+			#print(usable_mem, data_size, input_size, cases)
+				
+			bsz_tensor = torch.tensor([batch_size], device=global_device, dtype=torch.int32)
+
+			if cases == 0:
+				row = [f"{batch_size}", "OOM", "OOM", "OOM", "0", "False"]
+				rows.append(row)
+				break
+			for _ in range(cases):
+				modules.append(LinearMarlin(input_dim, cur_output_dim, sms=56, non_equal_division=False).to(device=global_device).eval())
+				inputs.append(torch.randn(batch_size, 1, input_dim, device=global_device))
+				
+			def forward(case_id):
+				modules[case_id](inputs[case_id], bsz_tensor)
+				
+			used_time = timing(forward, iters=cases)
+			bandwidth = (data_size+input_size+output_size)/used_time/1e6
+			flops = 2*batch_size*input_dim*cur_output_dim
+			tflops = flops/used_time/1e9
+			cur_sms = modules[0].sms
+			row = [f"{batch_size}", f"{tp}", f"{used_time}", f"{bandwidth}", f"{tflops}", f"{cases}", modules[0].padding, cur_sms]
+			rows.append(row)
+			print(f"{batch_size}", f"{tp}", f"{used_time}", f"{bandwidth}", f"{tflops}", f"{cases}", modules[0].padding, cur_sms)
+	
+	"""
+	with open(out_file, 'w', newline='') as csvfile:
+		csvwriter = csv.writer(csvfile)
+		csvwriter.writerow(headers)
+		for row in rows:
+			csvwriter.writerow(row)
+	"""
+	
+	"""
+	markdown_table = " | ".join(headers) + "\n"
+	markdown_table += " | ".join(["---"] * len(headers)) + "\n"
+	for row in rows:
+		markdown_table += " | ".join(row) + "\n"
+
+	print(markdown_table)
+	"""
+	#print("finish write file", out_file)
+	#print("-------------------------------------------------------------")
+
+if __name__ == "__main__":
+	
+	benchLinearMarlin(5120, 3584)
+	exit(0)
+	
+	max_batch = 1
+	cur_batch = 1
+
+
+	marlin_linear = LinearMarlin(5120, 3584)
+
+	input_tensor = torch.randn(max_batch, 1, 5120, device="cuda", dtype=torch.bfloat16)
+	bsz_tensor = torch.tensor([max_batch], device="cuda", dtype=torch.int32)
+
+	out_truth = marlin_linear(input_tensor, bsz_tensor)
+
+	print(out_truth)
+
+	g = torch.cuda.CUDAGraph()
+	with torch.cuda.graph(g):
+		out_buf = marlin_linear(input_tensor, bsz_tensor)
+	
+	for i in range(10000):
+		g.replay()
+	
+	#torch.testing.assert_close(out_buf, out_truth, rtol=1e-3, atol=1e-3)
+	
+	marlin_linear = LinearMarlin(5120, 3584)
+	g = torch.cuda.CUDAGraph()
+	with torch.cuda.graph(g):
+		out_buf = marlin_linear(input_tensor, bsz_tensor)
+	
+	new_input = torch.randn(cur_batch, 1, 5120, device="cuda", dtype=torch.bfloat16)
+	bsz_tensor.copy_(torch.tensor([cur_batch], device="cuda", dtype=torch.int32))
+	
+	new_out_truth = marlin_linear(new_input, bsz_tensor)
+	input_tensor[:cur_batch].copy_(new_input)
+	input_tensor[cur_batch:] = 0
+	
+	g.replay()
+	
+	torch.cuda.synchronize()
+
+	def printMinMax(tensor):
+		abs_tensor = torch.abs(tensor)
+
+		min_val = torch.min(abs_tensor)
+		max_val = torch.max(abs_tensor)
+
+		min_indices = (abs_tensor == min_val).nonzero(as_tuple=True)
+		max_indices = (abs_tensor == max_val).nonzero(as_tuple=True)
+
+		print(f"min: {min_val.item()}")
+		print(f"min idx: {min_indices}")
+		print(f"max: {max_val.item()}")
+		print(f"max idx: {max_indices}")
+
+	print(out_buf[:cur_batch].shape)
+	print(new_out_truth.shape)
+
+
+	printMinMax(out_buf[:cur_batch])
+	printMinMax(new_out_truth)
+
+	#torch.testing.assert_close(out_buf[:cur_batch, 0, :], new_out_truth[:cur_batch, 0, :], rtol=1e-3, atol=1e-3)

csrc/custom_marlin/utils/format24.py

+#
+# Modified by Roberto Lopez Castro (roberto.lopez.castro@udc.es).
+#
+
+import torch
+
+
+# This is PyTorch implementation of main part of reorder_meta()
+# function, from tools/util/include/cutlass/util/host_reorder.h file
+# of CUTLASS source tree.  Furthermore, CUTLASS template for sparse
+# GEMM decides upon layout of this matrix, and at the moment for the
+# sparse GEMM executed on tensor cores, this is layout described by
+# ColumnMajorInterleaved<2> data structure, in
+# include/cutlass/layout/matrix.h of CUTLASS source tree.  The
+# reordering of meta matrix into meta_reordered matrix calculated
+# according to these segments of CUTLASS code is re-implemented here.
+# Note that this calculation produces offsets for scattering metadata
+# matrix elements into reordered metadata matrix elements (or,
+# equivalently, for gathering reordered metadata matrix element back
+# into metadata matrix elements).
+def _calculate_meta_reordering_scatter_offsets(m, meta_ncols, meta_dtype,
+                                               device):
+    dst_rows = torch.arange(0, m, device=device)[:, None].repeat(1, meta_ncols)
+    dst_cols = torch.arange(0, meta_ncols, device=device).repeat(m, 1)
+
+    # Reorder the rows, then swizzle the 2x2 blocks.
+    group_x = 64
+    group_y = 32 if meta_dtype.itemsize == 2 else 16
+
+    dst_rows = (dst_rows // group_x * group_x + (dst_rows % 2) * 2 +
+                (dst_rows % 8) // 4 + ((dst_rows % group_y) % 4) // 2 * 32 +
+                ((dst_rows % group_x) // 8) * 4)
+
+    topright = ((dst_rows % 2 == 0) & (dst_cols % 2 == 1)).to(torch.int8)
+    bottomleft = ((dst_rows % 2 == 1) & (dst_cols % 2 == 0)).to(torch.int8)
+    dst_rows += topright - bottomleft
+    dst_cols -= topright - bottomleft
+
+    # Assumed that meta tensor is to be stored in CUTLASS
+    # InterleavedColumnMajor layout, and reverse engineered
+    # corresponding code to store values into this tensor.
+    interleave = 2
+    cols_maj = dst_cols // interleave
+    cols_min = dst_cols % interleave
+    return (cols_maj * m * interleave + dst_rows * interleave +
+            cols_min).view(-1)
+
+
+# This function converts dense matrix into sparse semi-structured
+# representation, producing "compressed" matrix, in the layout used by
+# CUTLASS backend, and corresponding metadata matrix.
+def sparse_semi_structured_from_dense_cutlass(dense):
+    if dense.dim() != 2:
+        raise RuntimeError(
+            f"Expected 2-dimensional dense tensor, got {dense.dim()}-dimensional tensor"  # noqa: E501
+        )
+
+    m, k = dense.shape
+    device = dense.device
+
+    meta_dtype = torch.int8
+    if dense.dtype == torch.int8:
+        meta_dtype = torch.int32
+    elif dense.dtype in [torch.half, torch.bfloat16, torch.float, torch.int32]:
+        meta_dtype = torch.int16
+    else:
+        raise RuntimeError(f"Invalid datatype {dense.dtype} of dense matrix")
+    quadbits_per_meta_elem = meta_dtype.itemsize * 8 // 4
+    if quadbits_per_meta_elem not in (4, 8):
+        raise RuntimeError(
+            "Invalid number of elements per meta element calculated")
+
+    if meta_dtype == torch.int32:
+        if m % 16 != 0:
+            raise RuntimeError(
+                f"Number of rows of dense matrix {m} must be divisible by 16")
+    else:
+        if m % 32 != 0:
+            raise RuntimeError(
+                f"Number of rows of dense matrix {m} must be divisible by 32")
+    if k % (4 * quadbits_per_meta_elem) != 0:
+        raise RuntimeError(
+            f"Number of columns of dense matrix {k} must be divisible by {4 * quadbits_per_meta_elem}"  # noqa: E501
+        )
+
+    if dense.dtype != torch.float:
+        ksparse = 4
+        dense_4 = dense.view(-1, k // ksparse, ksparse)
+        m0, m1, m2, m3 = (dense_4 != 0).unbind(-1)
+    else:
+        ksparse = 2
+        dense_2 = dense.view(-1, k // ksparse, ksparse)
+        m0, m2 = m1, m3 = (dense_2 != 0).unbind(-1)
+    meta_ncols = k // (ksparse * quadbits_per_meta_elem)
+
+    # Encoding quadruples of True/False values as follows:
+    #     [True,  True,  False, False] -> 0b0100
+    #     [True,  False, True,  False] -> 0b1000
+    #     [False, True,  True,  False] -> 0b1001
+    #     [True,  False, False, True ] -> 0b1100
+    #     [False, True,  False, True ] -> 0b1101
+    #     [False, False, True,  True ] -> 0b1110
+    # Thus, lower two bits in the encoding are index of the True value
+    # at the lowest index in the quadruple, and the higher two bits in
+    # the encoding are index of the other True value in the quadruple.
+    # In case there are less than two True values, than False value or
+    # values at some index or indices are considered True for the
+    # encoding.  In case there are more than two True values, then the
+    # excess True value(s) at some indices are considered False for
+    # the encoding.  The exact encodings used for these cases are as
+    # follows:
+    #     [False, False, False, False] -> 0b1110
+    #     [False, False, False, True ] -> 0b1110
+    #     [False, False, True,  False] -> 0b1110
+    #     [False, True,  False, False] -> 0b1001
+    #     [False, True,  True,  True ] -> 0b1101
+    #     [True,  False, False, False] -> 0b1000
+    #     [True,  False, True,  True ] -> 0b1100
+    #     [True,  True,  False, True ] -> 0b0100
+    #     [True,  True,  True,  False] -> 0b0100
+    #     [True,  True,  True,  True ] -> 0b0100
+    # These particular encodings are chosen, with the help of Espresso
+    # logic minimizer software, for the purpose of minimization of
+    # corresponding Boolean functions, that translate non-zero flags
+    # into encoding bits.  Note also possible choices for the first
+    # and last of these encodings were limited only to (0b0100,
+    # 0b1110), in order to produce valid encodings for 1:2 sparsity
+    # case.
+
+    expr0 = m0 & m1
+    expr1 = ~m0 & m1
+    expr2 = ~m0 & ~m1
+    bit0 = expr1
+    bit1 = expr2
+    bit2 = expr0 | expr2 | m3
+    bit3 = expr1 | ~m1
+    idxs0 = bit0 | (bit1.to(torch.int64) << 1)
+    idxs1 = bit2 | (bit3.to(torch.int64) << 1)
+
+    if dense.dtype != torch.float:
+        sparse0 = dense_4.gather(
+            -1, idxs0.unsqueeze(-1))  # type: ignore[possibly-undefined]
+        sparse1 = dense_4.gather(-1, idxs1.unsqueeze(-1))
+        sparse = torch.stack((sparse0, sparse1), dim=-1).view(m, k // 2)
+    else:
+        sparse = dense_2.gather(-1,
+                                idxs0.unsqueeze(-1) // 2).view(
+                                    m,
+                                    k // 2)  # type: ignore[possibly-undefined]
+
+    meta_4 = idxs0 | (idxs1 << 2)
+    meta_n = meta_4.view(
+        (-1, meta_ncols, quadbits_per_meta_elem)).to(meta_dtype)
+
+    if quadbits_per_meta_elem == 4:
+        meta = (meta_n[:, :, 0]
+                | (meta_n[:, :, 1] << 4)
+                | (meta_n[:, :, 2] << 8)
+                | (meta_n[:, :, 3] << 12))
+    elif quadbits_per_meta_elem == 8:
+        meta = (meta_n[:, :, 0]
+                | (meta_n[:, :, 1] << 4)
+                | (meta_n[:, :, 2] << 8)
+                | (meta_n[:, :, 3] << 12)
+                | (meta_n[:, :, 4] << 16)
+                | (meta_n[:, :, 5] << 20)
+                | (meta_n[:, :, 6] << 24)
+                | (meta_n[:, :, 7] << 28))
+
+    # Reorder meta tensor elements.
+    meta_reordered = meta.new_empty(
+        (m * meta_ncols, ))  # type: ignore[possibly-undefined]
+    meta_offsets = _calculate_meta_reordering_scatter_offsets(
+        m, meta_ncols, meta_dtype, device)
+    meta_reordered.scatter_(0, meta_offsets, meta.view(-1))
+
+    return (sparse, meta_reordered.view(m, meta_ncols))
+
+
+# This function performs reverse of the function above - it
+# reconstructs dense matrix from a pair of "compressed" matrix, given
+# in the layout used by CUTLASS backend, and accompanying metadata
+# matrix.
+def sparse_semi_structured_to_dense_cutlass(sparse, meta_reordered):
+    if sparse.dim() != 2:
+        raise RuntimeError(
+            f"Expected 2-dimensional sparse tensor, got {sparse.dim()}-dimensional tensor"  # noqa: E501
+        )
+
+    m, k = sparse.shape
+    device = sparse.device
+
+    if meta_reordered.dim() != 2:
+        raise RuntimeError(
+            f"Expected 2-dimensional meta tensor, got {meta_reordered.dim()}-dimensional tensor"  # noqa: E501
+        )
+    if meta_reordered.device != device:
+        raise RuntimeError(
+            f"Expected meta matrix to be on {device} device, got matrix on {meta_reordered.device} device"  # noqa: E501
+        )
+
+    meta_dtype = meta_reordered.dtype
+    if meta_dtype not in (torch.int16, torch.int32):
+        raise RuntimeError(f"Invalid datatype {meta_dtype} of meta matrix")
+    quadbits_per_meta_elem = meta_dtype.itemsize * 8 // 4
+
+    ksparse = 4 if sparse.dtype != torch.float else 2
+
+    meta_nrows, meta_ncols = meta_reordered.shape
+    if meta_nrows != m:
+        raise RuntimeError(
+            f"Number of rows of meta matrix {meta_nrows} must be equal to number of columns of spase matrix {m}"  # noqa: E501
+        )
+    if meta_ncols * ksparse * quadbits_per_meta_elem != 2 * k:
+        raise RuntimeError(
+            f"Number of columns of sparse matrix {k} different from the {meta_ncols * ksparse * quadbits_per_meta_elem // 2}, "  # noqa: E501
+            "expected according to the number of columns of meta matrix")
+
+    # Undo meta tensor elements reordering.
+    meta_offsets = _calculate_meta_reordering_scatter_offsets(
+        m, meta_ncols, meta_dtype, device)
+    meta = torch.gather(meta_reordered.view(-1), 0,
+                        meta_offsets).view(m, meta_ncols)
+
+    # Unpack sparse tensor back to original dense tensor, using
+    # information provided by meta tensor.  Note that torch.float
+    # datatype is handled pretty much the same as
+    # torch.half/torch.bfloat16, as metadata for a pair of torch.float
+    # value is encoded as if underlying 8 bytes contain four
+    # torch.half/torch.bfloat16 values, where either first two or last
+    # two are zeros.
+    meta_2 = torch.empty(
+        (m, meta_ncols, 2 * quadbits_per_meta_elem),
+        dtype=meta_dtype,
+        device=device,
+    )
+    if quadbits_per_meta_elem == 4:
+        meta_2[:, :, 0] = meta & 0b11
+        meta_2[:, :, 1] = (meta >> 2) & 0b11
+        meta_2[:, :, 2] = (meta >> 4) & 0b11
+        meta_2[:, :, 3] = (meta >> 6) & 0b11
+        meta_2[:, :, 4] = (meta >> 8) & 0b11
+        meta_2[:, :, 5] = (meta >> 10) & 0b11
+        meta_2[:, :, 6] = (meta >> 12) & 0b11
+        meta_2[:, :, 7] = (meta >> 14) & 0b11
+    elif quadbits_per_meta_elem == 8:
+        meta_2[:, :, 0] = meta & 0b11
+        meta_2[:, :, 1] = (meta >> 2) & 0b11
+        meta_2[:, :, 2] = (meta >> 4) & 0b11
+        meta_2[:, :, 3] = (meta >> 6) & 0b11
+        meta_2[:, :, 4] = (meta >> 8) & 0b11
+        meta_2[:, :, 5] = (meta >> 10) & 0b11
+        meta_2[:, :, 6] = (meta >> 12) & 0b11
+        meta_2[:, :, 7] = (meta >> 14) & 0b11
+        meta_2[:, :, 8] = (meta >> 16) & 0b11
+        meta_2[:, :, 9] = (meta >> 18) & 0b11
+        meta_2[:, :, 10] = (meta >> 20) & 0b11
+        meta_2[:, :, 11] = (meta >> 22) & 0b11
+        meta_2[:, :, 12] = (meta >> 24) & 0b11
+        meta_2[:, :, 13] = (meta >> 26) & 0b11
+        meta_2[:, :, 14] = (meta >> 28) & 0b11
+        meta_2[:, :, 15] = (meta >> 30) & 0b11
+
+    dense_offsets = meta_2.view(-1) + (
+        torch.arange(0, 2 * m * k // ksparse, device=device) * 4).view(
+            -1, 1).repeat(1, 2).view(-1)
+
+    dense = torch.zeros((m * 2 * k, ), dtype=sparse.dtype, device=device)
+    if sparse.dtype != torch.float:
+        # dense.scatter_(0, dense_offsets, sparse.view(-1))
+        dense.scatter_(0, dense_offsets, sparse.reshape(-1))
+    else:
+        dense.view(torch.half).scatter_(0, dense_offsets,
+                                        sparse.view(torch.half).view(-1))
+
+    return dense.view(m, 2 * k)
+
+
+def mask_creator(tensor):
+    """
+    Class for creating N:M sparsity masks.
+    Masks will be created using the N:M ratio, where for every block of 
+    M weights, N will be pruned based on ranked weight value. Each mask 
+    will correspond to the given tensor.
+
+    :param N: The number of weights in a group to keep
+    :param M: The size of a weight group
+    """
+    N = 2
+    M = 4
+
+    mask = None
+    # for i, tensor in enumerate(tensors):
+    if tensor.numel() % M != 0:
+        raise ValueError(
+            f"Tensor of size {tensor.shape} can't be evenly divided into "
+            f"{M} groups")
+
+    num_groups = tensor.numel() // M
+
+    # N:M sparsity for linear layers
+    tensor_temp = tensor.detach().abs().reshape(num_groups, M)
+    index = torch.argsort(tensor_temp, dim=1)[:, :int(M - N)]
+
+    w_b = torch.ones(tensor_temp.shape, device=tensor_temp.device)
+    mask = w_b.scatter_(dim=1, index=index, value=0).reshape(tensor.shape)
+
+    return mask
\ No newline at end of file

csrc/custom_marlin/utils/marlin_24_perms.py

+'''
+Date: 2024-11-08 02:46:07
+LastEditors: djw
+LastEditTime: 2024-11-08 02:46:41
+'''
+"""This file is used for /tests and /benchmarks"""
+from typing import Dict, List
+
+import numpy
+import torch
+
+
+# Precompute permutations for Marlin24 weight and scale shuffling # noqa: E501
+#
+# Marlin works on [16*2,64] tiles. The goal of the permutations is to reorder the weight data so that it is compatible noqa: # noqa: E501
+# with the tensor-core format that is described here:
+# https://docs.nvidia.com/cuda/parallel-thread-execution/index.html#matrix-fragments-for-mma-m16n8k16-with-floating-point-type # noqa: E501
+#
+# As a result of this reordering, the vector loads inside the kernel will get the data as it is needed for tensor-core # noqa: E501
+# (without the need to use ldmatrix instructions) # noqa: E501
+def get_perms_24(num_bits: int):
+    perm_list: List[int] = []
+    for i in range(32):
+        perm1: List[int] = []
+        col = i // 4
+        col_o = col // 2
+        for block in [0, 1]:
+            for row in [
+                    2 * (i % 4),
+                    2 * (i % 4) + 1,
+                    2 * (i % 4 + 4),
+                    2 * (i % 4 + 4) + 1,
+            ]:
+                perm1.append(16 * row + col_o * 256 + 8 * (col % 2) +
+                             4 * block)
+        for j in range(4):
+            perm_list.extend([p + 1 * j for p in perm1])
+    perm = numpy.array(perm_list)
+
+    if num_bits == 4:
+        interleave = numpy.array([0, 2, 4, 6, 1, 3, 5, 7])
+    elif num_bits == 8:
+        interleave = numpy.array([0, 2, 1, 3])
+    else:
+        raise ValueError("num_bits must be 4 or 8, got {}".format(num_bits))
+
+    perm = perm.reshape((-1, len(interleave)))[:, interleave].ravel()
+    perm = torch.from_numpy(perm)
+    scale_perm: List[int] = []
+    for i in range(8):
+        scale_perm.extend([i * 8 + j for j in [0, 4, 1, 5, 2, 6, 3, 7]])
+    scale_perm_single: List[int] = []
+    for i in range(8):
+        scale_perm_single.extend([8 * i + j for j in [0, 1, 2, 3, 4, 5, 6, 7]])
+    return perm, scale_perm, scale_perm_single
+
+
+marlin_24_perm: Dict[int, torch.Tensor] = {}
+marlin_24_scale_perm: Dict[int, List[int]] = {}
+marlin_24_scale_perm_single: Dict[int, List[int]] = {}
+for num_bits in [4, 8]:
+    perm_24, scale_perm_24, scale_perm_single_24 = get_perms_24(num_bits)
+    marlin_24_perm[num_bits] = perm_24
+    marlin_24_scale_perm[num_bits] = scale_perm_24
+    marlin_24_scale_perm_single[num_bits] = scale_perm_single_24
\ No newline at end of file

csrc/custom_marlin/utils/marlin_perms.py

+'''
+Date: 2024-11-08 02:46:47
+LastEditors: djw
+LastEditTime: 2024-11-08 02:46:55
+'''
+"""This file is used for /tests and /benchmarks"""
+from typing import Dict, List
+
+import numpy
+import torch
+
+
+# Precompute permutations for Marlin weight and scale shuffling # noqa: E501
+#
+# Marlin works on [16,64] tiles. The goal of the permutations is to reorder the weight data so that it is compatible noqa: # noqa: E501
+# with the tensor-core format that is described here:
+# https://docs.nvidia.com/cuda/parallel-thread-execution/index.html#matrix-fragments-for-mma-m16n8k16-with-floating-point-type # noqa: E501
+#
+# As a result of this reordering, the vector loads inside the kernel will get the data as it is needed for tensor-core # noqa: E501
+# (without the need to use ldmatrix instructions) # noqa: E501
+def get_perms(num_bits: int):
+    perm_list: List[int] = []
+    for i in range(32):
+        perm1: List[int] = []
+        col = i // 4
+        for block in [0, 1]:
+            for row in [
+                    2 * (i % 4),
+                    2 * (i % 4) + 1,
+                    2 * (i % 4 + 4),
+                    2 * (i % 4 + 4) + 1,
+            ]:
+                perm1.append(16 * row + col + 8 * block)
+        for j in range(4):
+            perm_list.extend([p + 256 * j for p in perm1])
+
+    perm = numpy.array(perm_list)
+
+    if num_bits == 4:
+        interleave = numpy.array([0, 2, 4, 6, 1, 3, 5, 7])
+    elif num_bits == 8:
+        interleave = numpy.array([0, 2, 1, 3])
+    else:
+        raise Exception("num_bits must be 4 or 8, got {}".format(num_bits))
+
+    perm = perm.reshape((-1, len(interleave)))[:, interleave].ravel()
+    perm = torch.from_numpy(perm)
+    scale_perm: List[int] = []
+    for i in range(8):
+        scale_perm.extend([i + 8 * j for j in range(8)])
+    scale_perm_single: List[int] = []
+    for i in range(4):
+        scale_perm_single.extend(
+            [2 * i + j for j in [0, 1, 8, 9, 16, 17, 24, 25]])
+    return perm, scale_perm, scale_perm_single
+
+
+marlin_perm: Dict[int, torch.Tensor] = {}
+marlin_scale_perm: Dict[int, List[int]] = {}
+marlin_scale_perm_single: Dict[int, List[int]] = {}
+for num_bits in [4, 8]:
+    perm, scale_perm, scale_perm_single = get_perms(num_bits)
+    marlin_perm[num_bits] = perm
+    marlin_scale_perm[num_bits] = scale_perm
+    marlin_scale_perm_single[num_bits] = scale_perm_single
\ No newline at end of file

csrc/custom_marlin/utils/marlin_utils.py

+"""This file is used for /tests and /benchmarks"""
+import random
+
+import numpy
+import torch
+
+from .format24 import (
+    mask_creator, sparse_semi_structured_from_dense_cutlass)
+from .marlin_24_perms import (
+    marlin_24_perm, marlin_24_scale_perm, marlin_24_scale_perm_single)
+from .marlin_perms import (
+    marlin_perm, marlin_scale_perm, marlin_scale_perm_single)
+from .quant_utils import (
+    get_pack_factor, quantize_weights, sort_weights, dequantize_weights)
+
+
+
+__cuda_arch = torch.cuda.get_device_capability()
+
+MARLIN_TILE = 16
+
+GPTQ_MARLIN_TILE = 16
+GPTQ_MARLIN_MIN_THREAD_N = 64
+GPTQ_MARLIN_MIN_THREAD_K = 128
+GPTQ_MARLIN_MAX_PARALLEL = 16
+
+GPTQ_MARLIN_SUPPORTED_NUM_BITS = [4, 8]
+GPTQ_MARLIN_SUPPORTED_GROUP_SIZES = [-1, 32, 64, 128]
+GPTQ_MARLIN_SUPPORTED_SYM = [True]
+
+def is_marlin_supported():
+    return __cuda_arch[0] >= 8
+
+
+def marlin_permute_weights(q_w, size_k, size_n, perm, tile=MARLIN_TILE):
+    assert q_w.shape == (size_k, size_n)
+    assert size_k % tile == 0, f"size_k = {size_k}, tile = {tile}"
+    assert size_n % tile == 0, f"size_k = {size_n}, tile = {tile}"
+
+    # Permute weights to 16x64 marlin tiles
+    q_w = q_w.reshape((size_k // tile, tile, size_n // tile, tile))
+    q_w = q_w.permute((0, 2, 1, 3))
+    q_w = q_w.reshape((size_k // tile, size_n * tile))
+
+    q_w = q_w.reshape((-1, perm.numel()))[:, perm].reshape(q_w.shape)
+
+    return q_w
+
+
+def marlin_weights(q_w, size_k, size_n, num_bits, perm):
+    # Permute
+    q_w = marlin_permute_weights(q_w, size_k, size_n, perm)
+
+    # Pack
+    pack_factor = get_pack_factor(num_bits)
+    orig_device = q_w.device
+
+    q_w = q_w.cpu().numpy().astype(numpy.uint32)
+
+    q_packed = numpy.zeros((q_w.shape[0], q_w.shape[1] // pack_factor),
+                           dtype=numpy.uint32)
+    for i in range(pack_factor):
+        q_packed |= q_w[:, i::pack_factor] << num_bits * i
+
+    q_packed = torch.from_numpy(q_packed.astype(numpy.int32)).to(orig_device)
+
+    return q_packed
+
+
+def marlin_permute_scales(s, size_k, size_n, group_size, scale_perm,
+                          scale_perm_single):
+    if group_size < size_k and group_size != -1:
+        s = s.reshape((-1, len(scale_perm)))[:, scale_perm]
+    else:
+        s = s.reshape((-1, len(scale_perm_single)))[:, scale_perm_single]
+    s = s.reshape((-1, size_n)).contiguous()
+
+    return s
+
+
+def marlin_quantize(
+    w: torch.Tensor,
+    num_bits: int,
+    group_size: int,
+    act_order: bool,
+):
+    size_k, size_n = w.shape
+
+    # Normalize group_size
+    if group_size == -1:
+        group_size = size_k
+    assert group_size <= size_k
+
+    # Quantize (and apply act_order if provided)
+    w_ref, q_w, s, g_idx, rand_perm = quantize_weights(w, num_bits, group_size,
+                                                       act_order)
+
+    # For act_order, sort the "weights" and "g_idx" so that group ids are
+    # increasing
+    sort_indices = torch.empty(0, dtype=torch.int, device=w.device)
+    if act_order:
+        q_w, g_idx, sort_indices = sort_weights(q_w, g_idx)
+
+    # Reformat to marlin
+    marlin_q_w = marlin_weights(q_w, size_k, size_n, num_bits,
+                                marlin_perm[num_bits])
+    marlin_s = marlin_permute_scales(s, size_k, size_n, group_size,
+                                     marlin_scale_perm[num_bits],
+                                     marlin_scale_perm_single[num_bits])
+
+    # Create result
+    res_list = [w_ref, marlin_q_w, marlin_s, g_idx, sort_indices, rand_perm]
+    for i in range(len(res_list)):
+        res_list[i] = res_list[i].to(w.device)
+
+    return res_list
+
+
+def inject_24(w, size_k, size_n):
+    assert w.shape == (size_k, size_n)
+
+    mask = mask_creator(w.t()).t().cuda().bool()
+
+    return (mask * w).contiguous(), mask.contiguous()
+
+
+def check_24(w, num_rows_to_sample=50, _verbose=False):
+    BLOCK_SIZE = 4
+    MAX_NON_ZEROS = 2
+
+    w = w.t().contiguous()
+
+    print("check_24: w.shape = {}".format(w.shape))
+
+    num_rows, num_cols = w.shape
+    sampled_row_idxs = random.choices(range(num_rows), k=num_rows_to_sample)
+    if _verbose:
+        print(f"Sampled row idxs = {sampled_row_idxs}")
+
+    total_segments = 0
+    non_24_segments = 0
+    for i in sampled_row_idxs:
+        for j in range(0, num_cols - BLOCK_SIZE, BLOCK_SIZE):
+            total_segments += 1
+            block = w[i, j:j + BLOCK_SIZE]
+            num_nonzero = torch.count_nonzero(block)
+            if num_nonzero > MAX_NON_ZEROS:
+                print("i = {} j = {} block = {}".format(i, j, block))
+                non_24_segments += 1
+
+    print(f"{non_24_segments} / {total_segments} do not have 2:4 structure.")
+
+
+def compress_quantized_24_weight(q_24, size_k, size_n, num_bits):
+    assert q_24.shape == (size_k, size_n)
+
+    # Remove zp to normalize over 0
+    max_q_val = (1 << num_bits) - 1
+    zp = (max_q_val + 1) // 2
+    q_24_no_zp = q_24 - zp
+
+    # Compress
+    q_24_no_zp = q_24_no_zp.t().contiguous()
+    q_24_no_zp_comp, meta = sparse_semi_structured_from_dense_cutlass(
+        q_24_no_zp)
+    q_24_no_zp_comp = q_24_no_zp_comp.t().contiguous()
+
+    # Restore zp
+    q_24_comp = q_24_no_zp_comp + zp
+
+    # Resize meta to its actual shape (without moving any data)
+    meta = meta.resize_(meta.shape[1] // 2, meta.shape[0] * 2)
+
+    return q_24_comp, meta
+
+
+def marlin_24_quantize(
+    w: torch.Tensor,
+    num_bits: int,
+    group_size: int,
+):
+    size_k, size_n = w.shape
+
+    # Normalize group_size
+    if group_size == -1:
+        group_size = size_k
+    assert group_size <= size_k
+
+    # Inject 2:4 sparsity
+    w_24, mask_24 = inject_24(w, size_k, size_n)
+
+    # Quantize
+    w_24_ref, q_w_24, s, g_idx, rand_perm = quantize_weights(w_24,
+                                                             num_bits,
+                                                             group_size,
+                                                             act_order=False)
+
+    # Compress quantized weight
+    q_w_24_comp, meta = compress_quantized_24_weight(q_w_24, size_k, size_n,
+                                                     num_bits)
+    size_k_comp = size_k // 2
+
+    # Reformat to marlin
+    marlin_24_q_w_comp = marlin_weights(q_w_24_comp, size_k_comp, size_n,
+                                        num_bits, marlin_24_perm[num_bits])
+    marlin_24_s = marlin_permute_scales(s, size_k, size_n, group_size,
+                                        marlin_24_scale_perm[num_bits],
+                                        marlin_24_scale_perm_single[num_bits])
+
+    # Create result
+    res_list = [w_24_ref, marlin_24_q_w_comp, meta, marlin_24_s]
+    for i in range(len(res_list)):
+        res_list[i] = res_list[i].to(w.device)
+
+    return res_list
+
+
+def compute_max_diff(output, output_ref):
+    return torch.mean(torch.abs(output - output_ref)) / torch.mean(
+        torch.abs(output_ref))
+
+
+class MarlinWorkspace:
+
+    def __init__(self, out_features, min_thread_n, max_parallel, device):
+        assert (out_features % min_thread_n == 0), (
+            "out_features = {} is undivisible by min_thread_n = {}".format(
+                out_features, min_thread_n))
+
+        max_workspace_size = ((out_features // min_thread_n) * max_parallel)
+
+        self.scratch = torch.zeros(max_workspace_size,
+                                   dtype=torch.int,
+                                   device=device)
\ No newline at end of file

csrc/custom_marlin/utils/quant_utils.py

+"""This file is used for /tests and /benchmarks"""
+import numpy
+import torch
+
+SUPPORTED_NUM_BITS = [4, 8]
+SUPPORTED_GROUP_SIZES = [-1, 32, 64, 128]
+
+
+def get_pack_factor(num_bits):
+    assert num_bits in SUPPORTED_NUM_BITS, f"Unsupported num_bits = {num_bits}"
+    return 32 // num_bits
+
+
+def permute_rows(q_w: torch.Tensor, w_ref: torch.Tensor, group_size: int):
+    assert q_w.shape == w_ref.shape
+
+    orig_device = q_w.device
+    k_size, _ = q_w.shape
+
+    g_idx = torch.zeros((k_size, ), dtype=torch.int32)
+    for i in range(k_size):
+        g_idx[i] = i // group_size
+
+    # Simulate act_order by doing a random permutation on K
+    rand_perm = torch.randperm(k_size)
+
+    g_idx = g_idx[rand_perm].contiguous()
+    q_w = q_w[rand_perm, :].contiguous()
+    w_ref = w_ref[rand_perm, :].contiguous()
+
+    return (
+        w_ref.to(device=orig_device),
+        q_w.to(device=orig_device),
+        g_idx.to(device=orig_device),
+        rand_perm.to(device=orig_device),
+    )
+
+
+# Function: Dequantize quantized weights
+def dequantize_weights(qweight, qzeros, scales, g_idx, bits=4, group_size=128, device='cuda:0'):
+    # Create a tensor for bitwise right shift operation
+    wf = torch.tensor(list(range(0, 32, bits)), dtype=torch.int32, device=device).unsqueeze(0)
+
+    # Apply bitwise right shift and convert qzeros to the appropriate type
+    zeros = torch.bitwise_right_shift(torch.unsqueeze(qzeros, 2).expand(-1, -1, 32 // bits), wf.unsqueeze(0)).to(torch.int16 if bits == 8 else torch.int8)
+    torch.bitwise_and(zeros, (2 ** bits) - 1, out=zeros)
+
+    # Reshape the zeros tensor
+    zeros = zeros + 1
+    zeros = zeros.reshape(-1, 1, zeros.shape[1] * zeros.shape[2])
+
+    # Reshape the scales tensor
+    scales = scales.reshape(-1, 1, scales.shape[-1])
+
+    # Similar bitwise right shift operation for qweight and reshape
+    weight = torch.bitwise_right_shift(torch.unsqueeze(qweight, 1).expand(-1, 32 // bits, -1), wf.unsqueeze(-1)).to(torch.int16 if bits == 8 else torch.int8)
+    torch.bitwise_and(weight, (2 ** bits) - 1, out=weight)
+    weight = weight.reshape(-1, group_size, weight.shape[2])
+
+    # Apply dequantization formula and reshape the final weight
+    weight = (scales * (weight - zeros))
+    weight = weight.reshape(weight.shape[0] * weight.shape[1], weight.shape[2])
+
+    # Return the transposed weight
+    return weight.transpose(0, 1)
+
+def quantize_weights(w: torch.Tensor, num_bits: int, group_size: int,
+                     act_order: bool):
+    orig_device = w.device
+    size_k, size_n = w.shape
+
+    assert w.is_floating_point(), "w must be float"
+    assert num_bits in SUPPORTED_NUM_BITS, f"Unsupported num_bits = {num_bits}"
+    assert group_size in SUPPORTED_GROUP_SIZES + [
+        size_k
+    ], f"Unsupported groupsize = {group_size}"
+
+    if group_size == -1:
+        group_size = size_k
+    assert group_size <= size_k
+
+    max_q_val = 2**num_bits - 1
+    half_q_val = (max_q_val + 1) // 2
+
+    # Reshape to [groupsize, -1]
+    if group_size < size_k:
+        w = w.view((-1, group_size, size_n))
+        w = w.permute(1, 0, 2)
+        w = w.reshape((group_size, -1))
+
+    # Compute scale for each group
+    s = torch.max(torch.abs(w), 0, keepdim=True)[0]
+    s *= 2 / max_q_val  # 2 => symmetric
+
+    # Quantize
+    q_w = torch.round(w / s).int()
+    q_w += half_q_val
+    q_w = torch.clamp(q_w, 0, max_q_val)
+
+    # Compute ref (dequantized)
+    w_ref = (q_w - half_q_val).half() * s
+
+    # Restore original shapes
+    if group_size < size_k:
+
+        def reshape_w(w):
+            w = w.reshape((group_size, -1, size_n))
+            w = w.permute(1, 0, 2)
+            w = w.reshape((size_k, size_n)).contiguous()
+            return w
+
+        q_w = reshape_w(q_w)
+        w_ref = reshape_w(w_ref)
+
+    s = s.reshape((-1, size_n)).contiguous()
+
+    # Apply act_order
+    g_idx = torch.empty(0, dtype=torch.int, device=w.device)
+    rand_perm = torch.empty(0, dtype=torch.int, device=w.device)
+    if act_order:
+        assert (
+            group_size < size_k
+        ), "For act_order, groupsize = {} must be less than size_k = {}".format(
+            group_size, size_k)
+
+        w_ref, q_w, g_idx, rand_perm = permute_rows(q_w, w_ref, group_size)
+
+    return (
+        w_ref.to(device=orig_device),
+        q_w.to(device=orig_device),
+        s.to(device=orig_device),
+        g_idx.to(device=orig_device),
+        rand_perm.to(device=orig_device),
+    )
+
+
+def sort_weights(q_w: torch.Tensor, g_idx: torch.Tensor):
+    orig_device = q_w.device
+
+    sort_indices = torch.argsort(g_idx).to(
+        dtype=torch.int32)  # Sort based on g_idx
+
+    g_idx = g_idx[sort_indices].contiguous()
+    q_w = q_w[sort_indices, :].contiguous()
+
+    return (
+        q_w.to(device=orig_device),
+        g_idx.to(device=orig_device),
+        sort_indices.to(device=orig_device),
+    )
+
+
+def gptq_pack(
+    q_w: torch.Tensor,
+    num_bits: int,
+    size_k: int,
+    size_n: int,
+):
+    assert q_w.shape == (size_k, size_n)
+
+    pack_factor = get_pack_factor(num_bits)
+    assert size_k % pack_factor == 0
+
+    orig_device = q_w.device
+
+    q_w = q_w.cpu().numpy().astype(numpy.uint32)
+
+    q_res = numpy.zeros((size_k // pack_factor, size_n), dtype=numpy.uint32)
+
+    for i in range(pack_factor):
+        q_res |= q_w[i::pack_factor, :] << num_bits * i
+
+    q_res = torch.from_numpy(q_res.astype(numpy.int32)).to(orig_device)
+    return q_res
+
+def gptq_unpack(
+    q_res: torch.Tensor,
+    num_bits: int,
+    size_k: int,
+    size_n: int,
+):
+    pack_factor = 32 // num_bits
+    assert size_k % pack_factor == 0
+
+    orig_device = q_res.device
+
+    q_res = q_res.cpu().numpy()
+
+    q_w = numpy.zeros((size_k, size_n), dtype=numpy.uint32)
+
+    for i in range(pack_factor):
+        q_w[i::pack_factor, :] = (q_res >> (num_bits * i)) & ((1 << num_bits) - 1)
+
+    q_w = torch.from_numpy(q_w.astype(numpy.int32)).to(orig_device)
+    return q_w
\ No newline at end of file

ktransformers/ktransformers_ext/CMakeLists.txt → csrc/ktransformers_ext/CMakeLists.txt

@@ -3,8 +3,16 @@ project(cpuinfer_ext VERSION 0.1.0)
 
 
 set(CMAKE_CXX_STANDARD 17)
-set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -O3 -ffast-math")
+
+
+set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -O3 -ffast-math -fopenmp")
+add_compile_definitions(_GLIBCXX_USE_CXX11_ABI=${_GLIBCXX_USE_CXX11_ABI})
 set(CMAKE_BUILD_TYPE "Release")
+# set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -g -ffast-math -fopenmp")
+# set(CMAKE_BUILD_TYPE "Debug")
+set(CMAKE_EXPORT_COMPILE_COMMANDS ON)
+
+
 include(CheckCXXCompilerFlag)
 set(CMAKE_POSITION_INDEPENDENT_CODE ON)
 
@@ -30,7 +38,7 @@ if (NOT MSVC)
     option(LLAMA_F16C                        "llama: enable F16C"                               OFF)
 endif()
 option(LLAMA_AVX512_FANCY_SIMD               "llama: enable AVX512-VL, AVX512-BW, AVX512-DQ, AVX512-VNNI"                        OFF)
-option(KTRANSFORMERS_USE_CUDA                "ktransformers: use CUDA"                          OFF)
+option(KTRANSFORMERS_USE_CUDA                "ktransformers: use CUDA"                          ON)
 option(KTRANSFORMERS_USE_MUSA                "ktransformers: use MUSA"                          OFF)
 option(KTRANSFORMERS_USE_ROCM                "ktransformers: use ROCM"                          OFF)
 
@@ -147,6 +155,7 @@ elseif (CMAKE_OSX_ARCHITECTURES STREQUAL "x86_64" OR CMAKE_GENERATOR_PLATFORM_LW
         endif()
     else()
         if (LLAMA_NATIVE)
+            list(APPEND ARCH_FLAGS -mfma -mavx -mavx2)
             list(APPEND ARCH_FLAGS -march=native)
         endif()
         if (LLAMA_F16C)
@@ -172,6 +181,7 @@ elseif (CMAKE_OSX_ARCHITECTURES STREQUAL "x86_64" OR CMAKE_GENERATOR_PLATFORM_LW
             list(APPEND ARCH_FLAGS -mavx512vnni)
         endif()
         if (LLAMA_AVX512_FANCY_SIMD)
+            message(STATUS "AVX512-VL, AVX512-BW, AVX512-DQ, AVX512-VNNI enabled")
             list(APPEND ARCH_FLAGS -mavx512vl)
             list(APPEND ARCH_FLAGS -mavx512bw)
             list(APPEND ARCH_FLAGS -mavx512dq)
@@ -238,9 +248,18 @@ if (WIN32)
     include_directories("$ENV{CUDA_PATH}/include")
     add_compile_definitions(KTRANSFORMERS_USE_CUDA=1)
 elseif (UNIX)
-    if (KTRANSFORMERS_USE_CUDA)
-        find_package(CUDA REQUIRED)
-        include_directories("${CUDA_INCLUDE_DIRS}")
+    if (NOT KTRANSFORMERS_USE_MUSA)
+        # find_package(CUDA REQUIRED)
+        # include_directories("${CUDA_INCLUDE_DIRS}")
+        include(CheckLanguage)
+        check_language(CUDA)
+        if(CMAKE_CUDA_COMPILER)
+            message(STATUS "CUDA detected")
+            find_package(CUDAToolkit REQUIRED)
+            include_directories(${CUDAToolkit_INCLUDE_DIRS})
+        endif()
+        message(STATUS "enabling CUDA")
+        enable_language(CUDA)
         add_compile_definitions(KTRANSFORMERS_USE_CUDA=1)
     endif()
 
@@ -278,19 +297,35 @@ aux_source_directory(${CMAKE_CURRENT_SOURCE_DIR}/cpu_backend SOURCE_DIR2)
 aux_source_directory(${CMAKE_CURRENT_SOURCE_DIR}/operators/llamafile SOURCE_DIR3)
 aux_source_directory(${CMAKE_CURRENT_SOURCE_DIR}/../../third_party/llamafile SOURCE_DIR4)
 aux_source_directory(${CMAKE_CURRENT_SOURCE_DIR}/operators/kvcache SOURCE_DIR5)
+
+
 set(ALL_SOURCES ${SOURCE_DIR1} ${SOURCE_DIR2} ${SOURCE_DIR3} ${SOURCE_DIR4} ${SOURCE_DIR5})
-message(STATUS "ALL_SOURCES: ${ALL_SOURCES}")
 
+file(GLOB_RECURSE FMT_SOURCES "${CMAKE_CURRENT_SOURCE_DIR}/*.cpp" "${CMAKE_CURRENT_SOURCE_DIR}/*.hpp" "${CMAKE_CURRENT_SOURCE_DIR}/*.h")
+
+add_custom_target(
+    format
+    COMMAND clang-format
+    -i
+    -style=file
+    ${FMT_SOURCES}
+    COMMENT "Running clang-format on all source files"
+)
+
+
+add_library(llamafile STATIC ${SOURCE_DIR4})
+
+message(STATUS "CMAKE_CXX_FLAGS: ${CMAKE_CXX_FLAGS}")
+message(STATUS "ARCH_FLAGS: ${ARCH_FLAGS}")
 pybind11_add_module(${PROJECT_NAME} MODULE ${ALL_SOURCES})
 target_link_libraries(${PROJECT_NAME} PRIVATE llama)
+
+
 if(WIN32)
     target_link_libraries(${PROJECT_NAME} PRIVATE "$ENV{CUDA_PATH}/lib/x64/cudart.lib")#CUDA::cudart
 elseif(UNIX)
-    if(KTRANSFORMERS_USE_CUDA)
-        if(NOT DEFINED ENV{CUDA_HOME} OR "$ENV{CUDA_HOME}" STREQUAL "")
-            set(ENV{CUDA_HOME} "/usr/local/cuda")
-        endif()
-        target_link_libraries(${PROJECT_NAME} PRIVATE "$ENV{CUDA_HOME}/lib64/libcudart.so")
+    if(NOT KTRANSFORMERS_USE_MUSA)
+        target_link_libraries(${PROJECT_NAME} PRIVATE "${CUDAToolkit_LIBRARY_DIR}/libcudart.so")
     endif()
     if (KTRANSFORMERS_USE_ROCM)
         add_compile_definitions(USE_HIP=1)
@@ -304,21 +339,28 @@ endif()
 
 # Define the USE_NUMA option
 option(USE_NUMA "Disable NUMA support" OFF)
+
 # Check if the USE_NUMA environment variable is set
 if(DEFINED ENV{USE_NUMA})
     set(USE_NUMA ON)
 endif()
-if (USE_NUMA)
+
+if(USE_NUMA)
     message(STATUS "NUMA support is enabled")
 else()
     message(STATUS "NUMA support is disabled")
 endif()
 
 find_library(NUMA_LIBRARY NAMES numa)
-if (NUMA_LIBRARY AND USE_NUMA)
+
+if(NUMA_LIBRARY AND USE_NUMA)
     message(STATUS "NUMA library found: ${NUMA_LIBRARY} - enabling NUMA support")
     target_link_libraries(${PROJECT_NAME} PRIVATE ${NUMA_LIBRARY})
     target_compile_definitions(${PROJECT_NAME} PRIVATE USE_NUMA)
 else()
-    message(STATUS "NUMA library not found or user not set USE_NUMA - disabling NUMA support")
-endif()
+    if(USE_NUMA)
+        message(FATAL_ERROR "NUMA library not found - maybe sudo apt install libnuma-dev")
+    else()
+        message(STATUS "NUMA library not found or user not set USE_NUMA - disabling NUMA support")
+    endif()
+endif()
\ No newline at end of file